First of all, we define all the libraries we need.
from matplotlib.ticker import PercentFormatter as _PercentFormatter
import matplotlib.pyplot as _plt
import numpy as _np
import pandas as _pd
import joblib as _jl
import glob as _glob
import os as _os
import re as _re
import time as _time
from multiprocessing import Pool as _Pool
from sklearn.preprocessing import OrdinalEncoder as _OrdinalEncoder, binarize as _binarize
from sklearn.metrics import confusion_matrix as _confusion_matrix, roc_curve as _roc_curve, classification_report as _classification_report, accuracy_score as _accuracy_score
from sklearn.model_selection import GridSearchCV as _GridSearchCV
from sklearn.svm import LinearSVC as _LinearSVC
from sklearn.ensemble import RandomForestClassifier as _RandomForestClassifier
from keras.models import Sequential as _Sequential, load_model as _load_model
from keras.layers import Dense as _Dense
_pd.set_option('display.max_columns', None)
Using TensorFlow backend.
Since we are going to use big datasets, and we'll need to load them more times, we define a commodity function that deletes all user defined variables, in order to free some memory.
def _del_all():
%reset_selective -f [^_]
We execute the code in recommendation_system_preprocessing.ipynb in order to
clean the data and to reduce the size of the dataset, using pickles instead of json and dropping unnecessary columns.
We explore the resulting datasets:
dataset_list = _glob.glob("../dataset/[!checked]*.pickle")
for d in dataset_list:
dataset = _pd.read_pickle(d)
f = _os.path.splitext(_os.path.basename(d))[0]
c = ", ".join(list(dataset.columns))
s = dataset.shape
print("Dataset '" + f + "':")
print("\tfeatures:", c)
print("\tshape:", s)
print()
Dataset 'all_checkin': features: business_id, date shape: (57402, 2) Dataset 'all_review': features: review_id, user_id, business_id, stars, useful, funny, cool, text, date shape: (4201684, 9) Dataset 'all_tips': features: restaurant_name, tips_date, user_id shape: (770878, 3) Dataset 'all_users': features: user_id, user_name, average_stars, yelping_since, review, years_of_elite, fans, useful, cool, funny, friends shape: (1148098, 11) Dataset 'restaurants': features: name, business_id, address, cuisine, postal_code, latitude, longitude, review_count, stars, OutdoorSeating, BusinessAcceptsCreditCards, RestaurantsDelivery, RestaurantsReservations, WiFi, Alcohol, categories, city, Monday_Open, Tuesday_Open, Wednesday_Open, Thursday_Open, Friday_Open, Saturday_Open, Sunday_Open, Monday_Close, Tuesday_Close, Wednesday_Close, Thursday_Close, Friday_Close, Saturday_Close, Sunday_Close shape: (59371, 31)
_del_all()
Then, in order to filter out deceptive reviews, that could alter the results
of our analysis, we load the model based on Support Vector Machine
defined in Yelp_sentiment_analysis/Scripts/fake_reviews.ipynb
by Zhiwei Zhang,
that has the best scores for accuracy, precision, recall and f1-score.
vectorizer = _jl.load('../models/tfidf_vectorizer.joblib')
svc = _jl.load('../models/fake_review_svc_model.joblib')
Now, we can apply this model to our data.
review = _pd.read_pickle("../dataset/all_review.pickle")
review.head()
| review_id | user_id | business_id | stars | useful | funny | cool | text | date | |
|---|---|---|---|---|---|---|---|---|---|
| 3 | yi0R0Ugj_xUx_Nek0-_Qig | dacAIZ6fTM6mqwW5uxkskg | ikCg8xy5JIg_NGPx-MSIDA | 5 | 0 | 0 | 0 | Went in for a lunch. Steak sandwich was delici... | 2018-01-09 20:56:38 |
| 5 | fdiNeiN_hoCxCMy2wTRW9g | w31MKYsNFMrjhWxxAb5wIw | eU_713ec6fTGNO4BegRaww | 4 | 0 | 0 | 0 | I'll be the first to admit that I was not exci... | 2013-01-20 13:25:59 |
| 6 | G7XHMxG0bx9oBJNECG4IFg | jlu4CztcSxrKx56ba1a5AQ | 3fw2X5bZYeW9xCz_zGhOHg | 3 | 5 | 4 | 5 | Tracy dessert had a big name in Hong Kong and ... | 2016-05-07 01:21:02 |
| 7 | 8e9HxxLjjqc9ez5ezzN7iQ | d6xvYpyzcfbF_AZ8vMB7QA | zvO-PJCpNk4fgAVUnExYAA | 1 | 3 | 1 | 1 | This place has gone down hill. Clearly they h... | 2010-10-05 19:12:35 |
| 10 | kbtscdyz6lvrtGjD1quQTg | FIk4lQQu1eTe2EpzQ4xhBA | 8mIrX_LrOnAqWsB5JrOojQ | 4 | 0 | 0 | 0 | Like walking back in time, every Saturday morn... | 2011-11-30 02:11:15 |
texts = list(review["text"])
X = vectorizer.transform(texts)
predictions = svc.predict(X)
print(type(predictions))
print("SVC predictions:", predictions)
<class 'numpy.ndarray'> SVC predictions: [1 1 1 ... 1 1 1]
Now we repeat the whole process with a different model that allows us to obtain real weights instead of a binary evaluation.
cal_svc = _jl.load('../models/fake_review_cal_svc_model.joblib')
cal_predictions = cal_svc.predict_proba(X)
print("Calibrated SVC predictions:\n", cal_predictions)
cal_predictions = _np.array([x[1] for x in cal_predictions])
print("Calibrated SVC predictions for class '1':\n", cal_predictions)
Calibrated SVC predictions: [[0.12112422 0.87887578] [0.011 0.989 ] [0.03538102 0.96461898] ... [0.36147536 0.63852464] [0.00379598 0.99620402] [0.00133685 0.99866315]] Calibrated SVC predictions for class '1': [0.87887578 0.989 0.96461898 ... 0.63852464 0.99620402 0.99866315]
print("columns before:\n", review.columns)
checked_review = review.assign(bin_truth_score=predictions, real_truth_score=cal_predictions)
print("columns after:\n", checked_review.columns)
columns before:
Index(['review_id', 'user_id', 'business_id', 'stars', 'useful', 'funny',
'cool', 'text', 'date'],
dtype='object')
columns after:
Index(['review_id', 'user_id', 'business_id', 'stars', 'useful', 'funny',
'cool', 'text', 'date', 'bin_truth_score', 'real_truth_score'],
dtype='object')
Let's see what we just obtained.
checked_review[['review_id', 'text', 'bin_truth_score', 'real_truth_score']].head()
| review_id | text | bin_truth_score | real_truth_score | |
|---|---|---|---|---|
| 3 | yi0R0Ugj_xUx_Nek0-_Qig | Went in for a lunch. Steak sandwich was delici... | 1 | 0.878876 |
| 5 | fdiNeiN_hoCxCMy2wTRW9g | I'll be the first to admit that I was not exci... | 1 | 0.989000 |
| 6 | G7XHMxG0bx9oBJNECG4IFg | Tracy dessert had a big name in Hong Kong and ... | 1 | 0.964619 |
| 7 | 8e9HxxLjjqc9ez5ezzN7iQ | This place has gone down hill. Clearly they h... | 1 | 0.867897 |
| 10 | kbtscdyz6lvrtGjD1quQTg | Like walking back in time, every Saturday morn... | 1 | 0.975404 |
data = checked_review['bin_truth_score']
_plt.hist(data, weights=_np.ones(len(data)) / len(data))
_plt.title("SVC labels distribution")
_plt.gca().yaxis.set_major_formatter(_PercentFormatter(1))
_plt.show()
data = checked_review['real_truth_score']
_plt.hist(data, weights=_np.ones(len(data)) / len(data))
_plt.title("Calibrated SVC labels distribution")
_plt.gca().yaxis.set_major_formatter(_PercentFormatter(1))
_plt.show()
Finally, we can save the new dataset without the text column,
in order to save space and computation time.
checked_review.drop(columns=['text'], inplace=True)
checked_review.to_pickle('../dataset/checked_review.pickle')
Check that everything has worked properly.
final_review = _pd.read_pickle('../dataset/checked_review.pickle')
print(final_review.columns)
final_review.head()
Index(['review_id', 'user_id', 'business_id', 'stars', 'useful', 'funny',
'cool', 'date', 'bin_truth_score', 'real_truth_score'],
dtype='object')
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 3 | yi0R0Ugj_xUx_Nek0-_Qig | dacAIZ6fTM6mqwW5uxkskg | ikCg8xy5JIg_NGPx-MSIDA | 5 | 0 | 0 | 0 | 2018-01-09 20:56:38 | 1 | 0.878876 |
| 5 | fdiNeiN_hoCxCMy2wTRW9g | w31MKYsNFMrjhWxxAb5wIw | eU_713ec6fTGNO4BegRaww | 4 | 0 | 0 | 0 | 2013-01-20 13:25:59 | 1 | 0.989000 |
| 6 | G7XHMxG0bx9oBJNECG4IFg | jlu4CztcSxrKx56ba1a5AQ | 3fw2X5bZYeW9xCz_zGhOHg | 3 | 5 | 4 | 5 | 2016-05-07 01:21:02 | 1 | 0.964619 |
| 7 | 8e9HxxLjjqc9ez5ezzN7iQ | d6xvYpyzcfbF_AZ8vMB7QA | zvO-PJCpNk4fgAVUnExYAA | 1 | 3 | 1 | 1 | 2010-10-05 19:12:35 | 1 | 0.867897 |
| 10 | kbtscdyz6lvrtGjD1quQTg | FIk4lQQu1eTe2EpzQ4xhBA | 8mIrX_LrOnAqWsB5JrOojQ | 4 | 0 | 0 | 0 | 2011-11-30 02:11:15 | 1 | 0.975404 |
_del_all()
Following this paper, we add some historical features to our dataset:
Before proceeding with the computation of the new features, we have to split the dataset in three parts:
M months;N months before;We pick m=3 and n=8, so the test set goes from 9/1/2018 to 11/30/2018, the training set goes
from 1/1/2018 to 8/31/2018, the history contains the remaining data, from 10/12/2004 to 12/31/2017.
review_all = _pd.read_pickle("../dataset/checked_review.pickle")
review_test = review_all[review_all['date']>=_np.datetime64('2018-09-01')]
review_train = review_all[(review_all['date']>=_np.datetime64('2018-01-01')) & (review_all['date']<_np.datetime64('2018-09-01'))]
# review_hist = review_all[review_all['date']<_np.datetime64('2018-01-01')]
review_test.to_pickle('../dataset/m2_n9/review_test.pickle')
review_train.to_pickle('../dataset/m2_n9/review_train.pickle')
# review_hist.to_pickle('../dataset/m2_n9/review_hist.pickle')
tips_all = _pd.read_pickle("../dataset/all_tips.pickle")
tips_test = tips_all[tips_all['tips_date']>=_np.datetime64('2018-10-01')]
tips_train = tips_all[(tips_all['tips_date']>=_np.datetime64('2018-01-01')) & (tips_all['tips_date']<_np.datetime64('2018-10-01'))]
tips_hist = tips_all[tips_all['tips_date']<_np.datetime64('2018-01-01')]
tips_test.to_pickle('../dataset/m2_n9/tips_test.pickle')
tips_train.to_pickle('../dataset/m2_n9/tips_train.pickle')
tips_hist.to_pickle('../dataset/m2_n9/tips_hist.pickle')
_del_all()
review_hist = _pd.read_pickle('../dataset/m2_n9/review_hist.pickle')
users = _pd.read_pickle("../dataset/all_users.pickle")
avg_stars = review_hist['stars'].mean()
users = users.assign(average_stars=avg_stars)
users = users.assign(num_reviews=0)
users = users.assign(average_stars_bin=avg_stars)
users = users.assign(num_reviews_bin=0)
users = users.assign(average_stars_real=avg_stars)
users = users.assign(num_reviews_real=0)
users = users.set_index('user_id')
users.head()
| user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | |||||||||||||||
| l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.703313 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| 4XChL029mKr5hydo79Ljxg | Jenna | 3.703313 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| bc8C_eETBWL0olvFSJJd0w | David | 3.703313 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| dD0gZpBctWGdWo9WlGuhlA | Angela | 3.703313 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| MM4RJAeH6yuaN8oZDSt0RA | Nancy | 3.703313 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | 0 | 3.703313 | 0 | 3.703313 | 0 |
def _f(grouped):
d = {}
d['num'] = grouped['stars'].size
d['stars'] = grouped['stars'].mean()
non_fake = _np.ma.masked_where(grouped['bin_truth_score']<=0, grouped['stars']).compressed()
d['num_bin'] = non_fake.size
d['stars_bin'] = non_fake.mean()
d['num_real'] = grouped['real_truth_score'].sum()
d['stars_real'] = _np.average(grouped['stars'], weights=grouped['real_truth_score'])
return _pd.Series(d, index=['num', 'stars', 'num_bin', 'stars_bin', 'num_real', 'stars_real'])
grouped_reviews = review_hist.groupby('user_id').apply(_f)
grouped_reviews.head()
import random
import statistics
current_milli_time = lambda: int(round(_time.time() * 1000))
def get_time(df):
us_id = random.choice(grouped_reviews.index)
x = random.randrange(1000)
t = current_milli_time()
df.loc[us_id, ["test"]] = x
t0 = current_milli_time()
return t0-t
def get_time_mul(df):
us_id = random.choice(grouped_reviews.index)
x = random.randrange(1000)
y = random.randrange(1000)
z = random.randrange(1000)
t = current_milli_time()
df.loc[us_id, ["test", "ciao", "prova"]] = [x, y, z]
t0 = current_milli_time()
return t0-t
def test():
df = users.copy()
df['test'] = -1
times = []
for i in range(1000):
times += [get_time(df)]
avg_time = statistics.mean(times)
del df
return avg_time
def test_mul():
df = users.copy()
df['test'] = -1
df['ciao'] = -1
df['prova'] = -1
times = []
for i in range(1000):
times += [get_time(df)]
avg_time = statistics.mean(times)
del df
return avg_time
def tot_time(ops, x, k):
time_millis = ops * k * x
hours = time_millis/1000/60/60
return hours
tot = len(grouped_reviews)
x = test()
print("hours:", tot_time(tot, x, 6))
x = test_mul()
print("hours mul:", tot_time(tot, x, 1))
hours: 53.065662455 hours mul: 8.774814544166667
count = 1
tot = len(grouped_reviews)
print("tot:", tot)
for index, row in grouped_reviews.iterrows():
uid = index
num = row['num']
stars = row['stars']
num_bin = row['num_bin']
stars_bin = row['stars_bin']
num_real = row['num_real']
stars_real = row['stars_real']
cols = ["num_reviews", "average_stars", "num_reviews_bin",
"average_stars_bin", "num_reviews_real", "average_stars_real"]
vals = [num, stars, num_bin, stars_bin, num_real, stars_real]
users.loc[uid, cols] = vals
count += 1
if count % 1000 == 0:
percent = (count/tot)*100
print("row {}/{} - {}%".format(count, tot, percent))
tot: 954447 row 1000/954447 - 0.10477271131870078% row 2000/954447 - 0.20954542263740156% row 3000/954447 - 0.31431813395610236% row 4000/954447 - 0.4190908452748031% row 5000/954447 - 0.5238635565935038% row 6000/954447 - 0.6286362679122047% row 7000/954447 - 0.7334089792309054% row 8000/954447 - 0.8381816905496062% row 9000/954447 - 0.942954401868307% row 10000/954447 - 1.0477271131870076% [...] row 945000/954447 - 99.01021219617223% row 946000/954447 - 99.11498490749094% row 947000/954447 - 99.21975761880964% row 948000/954447 - 99.32453033012834% row 949000/954447 - 99.42930304144704% row 950000/954447 - 99.53407575276574% row 951000/954447 - 99.63884846408445% row 952000/954447 - 99.74362117540313% row 953000/954447 - 99.84839388672184% row 954000/954447 - 99.95316659804054%
users = users.reset_index()
users.to_pickle('../dataset/m2_n9/users.pickle')
_del_all()
restaurants = _pd.read_pickle("../dataset/restaurants.pickle")
review_hist = _pd.read_pickle('../dataset/m2_n9/review_hist.pickle')
avg_stars = review_hist['stars'].mean()
restaurants = restaurants.assign(average_stars=avg_stars)
restaurants = restaurants.assign(num_reviews=0)
restaurants = restaurants.assign(average_stars_bin=avg_stars)
restaurants = restaurants.assign(num_reviews_bin=0)
restaurants = restaurants.assign(average_stars_real=avg_stars)
restaurants = restaurants.assign(num_reviews_real=0)
restaurants = restaurants.set_index('business_id')
restaurants.head()
| name | address | cuisine | postal_code | latitude | longitude | review_count | stars | OutdoorSeating | BusinessAcceptsCreditCards | ... | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| business_id | |||||||||||||||||||||
| QXAEGFB4oINsVuTFxEYKFQ | Emerald Chinese Restaurant | 30 Eglinton Avenue W | Chinese | L5R 3E7 | 43.605499 | -79.652289 | 128 | 2.5 | False | NaN | ... | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 3.703313 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| gnKjwL_1w79qoiV3IC_xQQ | Musashi Japanese Restaurant | 10110 Johnston Rd, Ste 15 | Japanese | 28210 | 35.092564 | -80.859132 | 170 | 4.0 | False | True | ... | 21:30:00 | 22:00:00 | 22:00:00 | 21:00:00 | 3.703313 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| 1Dfx3zM-rW4n-31KeC8sJg | Taco Bell | 2450 E Indian School Rd | Mexican | 85016 | 33.495194 | -112.028588 | 18 | 3.0 | False | True | ... | 01:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 3.703313 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| fweCYi8FmbJXHCqLnwuk8w | Marco's Pizza | 5981 Andrews Rd | Italian | 44060 | 41.708520 | -81.359556 | 16 | 4.0 | False | True | ... | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 3.703313 | 0 | 3.703313 | 0 | 3.703313 | 0 |
| PZ-LZzSlhSe9utkQYU8pFg | Carluccio's Tivoli Gardens | 1775 E Tropicana Ave, Ste 29 | Italian | 89119 | 36.100016 | -115.128529 | 40 | 4.0 | False | True | ... | NaT | NaT | NaT | NaT | 3.703313 | 0 | 3.703313 | 0 | 3.703313 | 0 |
5 rows × 36 columns
grouped_reviews = review_hist.groupby('business_id').apply(_f)
grouped_reviews.head()
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\ipykernel_launcher.py:10: RuntimeWarning: Mean of empty slice. # Remove the CWD from sys.path while we load stuff. C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\numpy\core\_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount)
| num | stars | num_bin | stars_bin | num_real | stars_real | |
|---|---|---|---|---|---|---|
| business_id | ||||||
| --1UhMGODdWsrMastO9DZw | 22.0 | 4.227273 | 14.0 | 4.214286 | 16.978214 | 4.267477 |
| --6MefnULPED_I942VcFNA | 38.0 | 3.157895 | 32.0 | 3.218750 | 33.948759 | 3.218815 |
| --9e1ONYQuAa-CB_Rrw7Tw | 1504.0 | 4.107048 | 1216.0 | 4.088816 | 1205.738732 | 4.092415 |
| --DaPTJW3-tB1vP-PfdTEg | 40.0 | 3.650000 | 33.0 | 3.575758 | 34.780500 | 3.642790 |
| --FBCX-N37CMYDfs790Bnw | 122.0 | 3.737705 | 90.0 | 3.611111 | 93.369750 | 3.646290 |
count = 1
tot = len(grouped_reviews)
print("tot:", tot)
for index, row in grouped_reviews.iterrows():
uid = index
num = row['num']
stars = row['stars']
num_bin = row['num_bin']
stars_bin = row['stars_bin']
num_real = row['num_real']
stars_real = row['stars_real']
cols = ["num_reviews", "average_stars", "num_reviews_bin",
"average_stars_bin", "num_reviews_real", "average_stars_real"]
vals = [num, stars, num_bin, stars_bin, num_real, stars_real]
restaurants.loc[uid, cols] = vals
count += 1
if count % 1000 == 0:
percent = (count/tot)*100
print("row {}/{} - {}%".format(count, tot, percent))
tot: 56850 row 1000/56850 - 1.759014951627089% row 2000/56850 - 3.518029903254178% row 3000/56850 - 5.277044854881266% row 4000/56850 - 7.036059806508356% row 5000/56850 - 8.795074758135444% row 6000/56850 - 10.554089709762533% row 7000/56850 - 12.313104661389621% row 8000/56850 - 14.072119613016712% row 9000/56850 - 15.8311345646438% row 10000/56850 - 17.590149516270888% [...] row 47000/56850 - 82.67370272647318% row 48000/56850 - 84.43271767810026% row 49000/56850 - 86.19173262972735% row 50000/56850 - 87.95074758135443% row 51000/56850 - 89.70976253298153% row 52000/56850 - 91.46877748460862% row 53000/56850 - 93.22779243623572% row 54000/56850 - 94.9868073878628% row 55000/56850 - 96.74582233948989% row 56000/56850 - 98.50483729111697%
restaurants = restaurants.reset_index()
restaurants.to_pickle('../dataset/m2_n9/restaurants.pickle')
_del_all()
restaurants = _pd.read_pickle('../dataset/m2_n9/restaurants.pickle')
restaurants.head()
| business_id | name | address | cuisine | postal_code | latitude | longitude | review_count | stars | OutdoorSeating | ... | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | QXAEGFB4oINsVuTFxEYKFQ | Emerald Chinese Restaurant | 30 Eglinton Avenue W | Chinese | L5R 3E7 | 43.605499 | -79.652289 | 128 | 2.5 | False | ... | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 2.726496 | 117.0 | 2.718750 | 96.0 | 2.730197 | 95.873087 |
| 1 | gnKjwL_1w79qoiV3IC_xQQ | Musashi Japanese Restaurant | 10110 Johnston Rd, Ste 15 | Japanese | 28210 | 35.092564 | -80.859132 | 170 | 4.0 | False | ... | 21:30:00 | 22:00:00 | 22:00:00 | 21:00:00 | 4.063291 | 158.0 | 4.094203 | 138.0 | 4.067541 | 139.112078 |
| 2 | 1Dfx3zM-rW4n-31KeC8sJg | Taco Bell | 2450 E Indian School Rd | Mexican | 85016 | 33.495194 | -112.028588 | 18 | 3.0 | False | ... | 01:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 3.125000 | 16.0 | 2.769231 | 13.0 | 2.847327 | 12.604125 |
| 3 | fweCYi8FmbJXHCqLnwuk8w | Marco's Pizza | 5981 Andrews Rd | Italian | 44060 | 41.708520 | -81.359556 | 16 | 4.0 | False | ... | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 4.230769 | 13.0 | 4.166667 | 12.0 | 4.142021 | 10.965903 |
| 4 | PZ-LZzSlhSe9utkQYU8pFg | Carluccio's Tivoli Gardens | 1775 E Tropicana Ave, Ste 29 | Italian | 89119 | 36.100016 | -115.128529 | 40 | 4.0 | False | ... | NaT | NaT | NaT | NaT | 4.097561 | 41.0 | 4.212121 | 33.0 | 4.167159 | 33.655622 |
5 rows × 37 columns
restaurants.columns
Index(['business_id', 'name', 'address', 'cuisine', 'postal_code', 'latitude',
'longitude', 'review_count', 'stars', 'OutdoorSeating',
'BusinessAcceptsCreditCards', 'RestaurantsDelivery',
'RestaurantsReservations', 'WiFi', 'Alcohol', 'categories', 'city',
'Monday_Open', 'Tuesday_Open', 'Wednesday_Open', 'Thursday_Open',
'Friday_Open', 'Saturday_Open', 'Sunday_Open', 'Monday_Close',
'Tuesday_Close', 'Wednesday_Close', 'Thursday_Close', 'Friday_Close',
'Saturday_Close', 'Sunday_Close', 'average_stars', 'num_reviews',
'average_stars_bin', 'num_reviews_bin', 'average_stars_real',
'num_reviews_real'],
dtype='object')
review_hist = _pd.read_pickle('../dataset/m2_n9/review_hist.pickle')
review_hist.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 5 | fdiNeiN_hoCxCMy2wTRW9g | w31MKYsNFMrjhWxxAb5wIw | eU_713ec6fTGNO4BegRaww | 4 | 0 | 0 | 0 | 2013-01-20 13:25:59 | 1 | 0.989000 |
| 6 | G7XHMxG0bx9oBJNECG4IFg | jlu4CztcSxrKx56ba1a5AQ | 3fw2X5bZYeW9xCz_zGhOHg | 3 | 5 | 4 | 5 | 2016-05-07 01:21:02 | 1 | 0.964619 |
| 7 | 8e9HxxLjjqc9ez5ezzN7iQ | d6xvYpyzcfbF_AZ8vMB7QA | zvO-PJCpNk4fgAVUnExYAA | 1 | 3 | 1 | 1 | 2010-10-05 19:12:35 | 1 | 0.867897 |
| 10 | kbtscdyz6lvrtGjD1quQTg | FIk4lQQu1eTe2EpzQ4xhBA | 8mIrX_LrOnAqWsB5JrOojQ | 4 | 0 | 0 | 0 | 2011-11-30 02:11:15 | 1 | 0.975404 |
| 11 | -I5umRTkhw15RqpKMl_o1Q | -mA3-1mN4JIEkqOtdbNXCQ | mRUVMJkUGxrByzMQ2MuOpA | 1 | 0 | 1 | 0 | 2017-12-15 23:27:08 | 1 | 0.972826 |
joined_reviews = review_hist.join(restaurants.set_index('business_id'), on = 'business_id', lsuffix='_review', rsuffix='_rest')
joined_reviews.head()
| review_id | user_id | business_id | stars_review | useful | funny | cool | date | bin_truth_score | real_truth_score | ... | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | fdiNeiN_hoCxCMy2wTRW9g | w31MKYsNFMrjhWxxAb5wIw | eU_713ec6fTGNO4BegRaww | 4 | 0 | 0 | 0 | 2013-01-20 13:25:59 | 1 | 0.989000 | ... | 22:00:00 | 23:00:00 | 23:00:00 | NaT | 3.642276 | 123.0 | 3.594059 | 101.0 | 3.596369 | 99.505441 |
| 6 | G7XHMxG0bx9oBJNECG4IFg | jlu4CztcSxrKx56ba1a5AQ | 3fw2X5bZYeW9xCz_zGhOHg | 3 | 5 | 4 | 5 | 2016-05-07 01:21:02 | 1 | 0.964619 | ... | 00:00:00 | 02:00:00 | 02:00:00 | 00:00:00 | 3.292453 | 106.0 | 3.320000 | 100.0 | 3.289493 | 94.480786 |
| 7 | 8e9HxxLjjqc9ez5ezzN7iQ | d6xvYpyzcfbF_AZ8vMB7QA | zvO-PJCpNk4fgAVUnExYAA | 1 | 3 | 1 | 1 | 2010-10-05 19:12:35 | 1 | 0.867897 | ... | 22:00:00 | 23:00:00 | 23:00:00 | 23:00:00 | 2.655172 | 29.0 | 2.875000 | 24.0 | 2.727490 | 23.413530 |
| 10 | kbtscdyz6lvrtGjD1quQTg | FIk4lQQu1eTe2EpzQ4xhBA | 8mIrX_LrOnAqWsB5JrOojQ | 4 | 0 | 0 | 0 | 2011-11-30 02:11:15 | 1 | 0.975404 | ... | 23:00:00 | 00:00:00 | 00:00:00 | 23:00:00 | 4.491259 | 1144.0 | 4.499535 | 1075.0 | 4.502904 | 1031.313358 |
| 11 | -I5umRTkhw15RqpKMl_o1Q | -mA3-1mN4JIEkqOtdbNXCQ | mRUVMJkUGxrByzMQ2MuOpA | 1 | 0 | 1 | 0 | 2017-12-15 23:27:08 | 1 | 0.972826 | ... | 00:00:00 | 02:00:00 | 02:00:00 | 00:00:00 | 3.666667 | 12.0 | 3.833333 | 6.0 | 3.650460 | 7.482296 |
5 rows × 46 columns
categories = ', '.join(list(restaurants['categories'].unique()))
categories = categories.split(', ')
print(len(categories))
cat = []
for h in categories:
if h not in cat:
cat.append(h)
print(len(cat))
cuisines = ', '.join(list(restaurants['cuisine'].unique()))
cuisines = cuisines.split(', ')
print(len(cuisines))
_cuisines_unique = []
for cuisine in cuisines:
if not cuisine in _cuisines_unique:
_cuisines_unique.append(cuisine)
print("Number of cuisines: {0}".format(len(_cuisines_unique)))
print(_cuisines_unique)
173884 761 249 Number of cuisines: 10 ['Chinese', 'Japanese', 'Mexican', 'Italian', 'Others', 'American', 'Korean', 'Mediterranean', 'Thai', 'Asian Fusion']
joined_reviews.to_pickle('../dataset/m2_n9/join_restaurants_reviewhist.pickle')
_del_all()
Checkpoint
joined_reviews = _pd.read_pickle('../dataset/m2_n9/join_restaurants_reviewhist.pickle')
joined_reviews.head()
| index | review_id | user_id | business_id | stars_review | useful | funny | cool | date | bin_truth_score | ... | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 5 | fdiNeiN_hoCxCMy2wTRW9g | w31MKYsNFMrjhWxxAb5wIw | eU_713ec6fTGNO4BegRaww | 4 | 0 | 0 | 0 | 2013-01-20 13:25:59 | 1 | ... | 22:00:00 | 23:00:00 | 23:00:00 | NaT | 3.642276 | 123.0 | 3.594059 | 101.0 | 3.596369 | 99.505441 |
| 1 | 6 | G7XHMxG0bx9oBJNECG4IFg | jlu4CztcSxrKx56ba1a5AQ | 3fw2X5bZYeW9xCz_zGhOHg | 3 | 5 | 4 | 5 | 2016-05-07 01:21:02 | 1 | ... | 00:00:00 | 02:00:00 | 02:00:00 | 00:00:00 | 3.292453 | 106.0 | 3.320000 | 100.0 | 3.289493 | 94.480786 |
| 2 | 7 | 8e9HxxLjjqc9ez5ezzN7iQ | d6xvYpyzcfbF_AZ8vMB7QA | zvO-PJCpNk4fgAVUnExYAA | 1 | 3 | 1 | 1 | 2010-10-05 19:12:35 | 1 | ... | 22:00:00 | 23:00:00 | 23:00:00 | 23:00:00 | 2.655172 | 29.0 | 2.875000 | 24.0 | 2.727490 | 23.413530 |
| 3 | 10 | kbtscdyz6lvrtGjD1quQTg | FIk4lQQu1eTe2EpzQ4xhBA | 8mIrX_LrOnAqWsB5JrOojQ | 4 | 0 | 0 | 0 | 2011-11-30 02:11:15 | 1 | ... | 23:00:00 | 00:00:00 | 00:00:00 | 23:00:00 | 4.491259 | 1144.0 | 4.499535 | 1075.0 | 4.502904 | 1031.313358 |
| 4 | 11 | -I5umRTkhw15RqpKMl_o1Q | -mA3-1mN4JIEkqOtdbNXCQ | mRUVMJkUGxrByzMQ2MuOpA | 1 | 0 | 1 | 0 | 2017-12-15 23:27:08 | 1 | ... | 00:00:00 | 02:00:00 | 02:00:00 | 00:00:00 | 3.666667 | 12.0 | 3.833333 | 6.0 | 3.650460 | 7.482296 |
5 rows × 47 columns
# joined_reviews = joined_reviews.reset_index()
joined_reviews = joined_reviews[['review_id', 'user_id', 'business_id', 'bin_truth_score', 'real_truth_score', 'cuisine', 'stars_review']]
joined_reviews.head()
| review_id | user_id | business_id | bin_truth_score | real_truth_score | cuisine | stars_review | |
|---|---|---|---|---|---|---|---|
| 0 | fdiNeiN_hoCxCMy2wTRW9g | w31MKYsNFMrjhWxxAb5wIw | eU_713ec6fTGNO4BegRaww | 1 | 0.989000 | Italian | 4 |
| 1 | G7XHMxG0bx9oBJNECG4IFg | jlu4CztcSxrKx56ba1a5AQ | 3fw2X5bZYeW9xCz_zGhOHg | 1 | 0.964619 | Chinese | 3 |
| 2 | 8e9HxxLjjqc9ez5ezzN7iQ | d6xvYpyzcfbF_AZ8vMB7QA | zvO-PJCpNk4fgAVUnExYAA | 1 | 0.867897 | American | 1 |
| 3 | kbtscdyz6lvrtGjD1quQTg | FIk4lQQu1eTe2EpzQ4xhBA | 8mIrX_LrOnAqWsB5JrOojQ | 1 | 0.975404 | Others | 4 |
| 4 | -I5umRTkhw15RqpKMl_o1Q | -mA3-1mN4JIEkqOtdbNXCQ | mRUVMJkUGxrByzMQ2MuOpA | 1 | 0.972826 | American | 1 |
#cuisines_unique = ['Chinese', 'Japanese', 'Mexican', 'Italian', 'Others', 'American', 'Korean', 'Mediterranean', 'Thai', 'Asian Fusion']
def each_cuisine_ratings(grouped):
d = {}
index = []
for cuisine in _cuisines_unique:
cuisine_av = cuisine + "_av"
cuisine_records = _np.ma.masked_where(~grouped['cuisine'].str.contains(cuisine), grouped['stars_review']).compressed()
d[cuisine_av] = cuisine_records.mean()
index.append(cuisine_av)
# print("cuisine_av done")
for cuisine in _cuisines_unique:
cuisine_av_bin = cuisine + "_av_bin"
#non_fake = _np.ma.masked_where(grouped['bin_truth_score'] < 0, grouped).compressed()
non_fake = grouped[grouped['bin_truth_score'] > 0]
cuisine_records = _np.ma.masked_where(~non_fake['cuisine'].str.contains(cuisine), non_fake['stars_review']).compressed()
d[cuisine_av_bin] = cuisine_records.mean()
index.append(cuisine_av_bin)
# print("cuisine_av_bin done")
for cuisine in _cuisines_unique:
cuisine_av_real = cuisine + "_av_real"
cuisine_records = _np.ma.masked_where(~grouped['cuisine'].str.contains(cuisine), grouped['stars_review']).compressed()
cuisine_truth_score = _np.ma.masked_where(~grouped['cuisine'].str.contains(cuisine), grouped['real_truth_score']).compressed()
d[cuisine_av_real] = _np.ma.average(cuisine_records, weights = cuisine_truth_score)
index.append(cuisine_av_real)
# print("cuisine_av_real done")
return _pd.Series(d, index = index)
grouped_reviews = joined_reviews.groupby('user_id').apply(each_cuisine_ratings)
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\ipykernel_launcher.py:8: RuntimeWarning: Mean of empty slice. C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\numpy\core\_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\ipykernel_launcher.py:17: RuntimeWarning: Mean of empty slice. C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\numpy\ma\extras.py:607: RuntimeWarning: invalid value encountered in double_scalars avg = np.multiply(a, wgt, dtype=result_dtype).sum(axis)/scl
grouped_reviews.head()
| Chinese_av | Japanese_av | Mexican_av | Italian_av | Others_av | American_av | Korean_av | Mediterranean_av | Thai_av | Asian Fusion_av | ... | Chinese_av_real | Japanese_av_real | Mexican_av_real | Italian_av_real | Others_av_real | American_av_real | Korean_av_real | Mediterranean_av_real | Thai_av_real | Asian Fusion_av_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | |||||||||||||||||||||
| ---1lKK3aKOuomHnwAkAow | NaN | 2.333333 | 3.666667 | 3.166667 | 4.352941 | 3.9375 | 4.0 | NaN | 3.25 | 3.5 | ... | NaN | 2.035809 | 3.720609 | 3.226154 | 4.524134 | 3.963953 | 4.0 | NaN | 3.134858 | 3.443446 |
| ---PLwSf5gKdIoVnyRHgBA | NaN | NaN | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | NaN |
| ---cu1hq55BP9DWVXXKHZg | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | ... | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 |
| ---udAKDsn0yQXmzbWQNSw | NaN | NaN | NaN | NaN | 5.000000 | NaN | NaN | NaN | 4.00 | NaN | ... | NaN | NaN | NaN | NaN | 5.000000 | NaN | NaN | NaN | 4.000000 | NaN |
| --0RtXvcOIE4XbErYca6Rw | NaN | NaN | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN |
5 rows × 30 columns
Checkpoint 2
users = _pd.read_pickle('../dataset/m2_n9/users.pickle')
users.head()
| user_id | user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | 2.0 | 3.000000 | 2.0 | 3.017247 | 1.957334 |
| 1 | 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | 12.0 | 3.777778 | 9.0 | 3.626019 | 9.280533 |
| 2 | bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | 13.0 | 3.500000 | 12.0 | 3.326528 | 10.350275 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | 1.0 | NaN | 0.0 | 5.000000 | 0.329341 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | 5.0 | 4.400000 | 5.0 | 4.378799 | 4.720870 |
users = users.assign(av_rat_chinese_cuisine = _np.nan, av_rat_japanese_cuisine = _np.nan, av_rat_mexican_cuisine = _np.nan,
av_rat_italian_cuisine = _np.nan, av_rat_others_cuisine = _np.nan, av_rat_american_cuisine = _np.nan,
av_rat_korean_cuisine = _np.nan, av_rat_mediterranean_cuisine = _np.nan, av_rat_thai_cuisine = _np.nan,
av_rat_asianfusion_cuisine = _np.nan)
users = users.assign(av_rat_chinese_cuisine_bin = _np.nan, av_rat_japanese_cuisine_bin = _np.nan, av_rat_mexican_cuisine_bin = _np.nan,
av_rat_italian_cuisine_bin = _np.nan, av_rat_others_cuisine_bin = _np.nan, av_rat_american_cuisine_bin = _np.nan,
av_rat_korean_cuisine_bin = _np.nan, av_rat_mediterranean_cuisine_bin = _np.nan, av_rat_thai_cuisine_bin = _np.nan,
av_rat_asianfusion_cuisine_bin = _np.nan)
users = users.assign(av_rat_chinese_cuisine_real = _np.nan, av_rat_japanese_cuisine_real = _np.nan, av_rat_mexican_cuisine_real = _np.nan,
av_rat_italian_cuisine_real = _np.nan, av_rat_others_cuisine_real = _np.nan, av_rat_american_cuisine_real = _np.nan,
av_rat_korean_cuisine_real = _np.nan, av_rat_mediterranean_cuisine_real = _np.nan, av_rat_thai_cuisine_real = _np.nan,
av_rat_asianfusion_cuisine_real = _np.nan)
users = users.set_index('user_id')
users.head()
| user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | ... | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | |||||||||||||||||||||
| l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 45 columns
grouped_reviews = _pd.read_pickle('../dataset/m2_n9/grouped_reviews.pickle')
grouped_reviews.head()
| Chinese_av | Japanese_av | Mexican_av | Italian_av | Others_av | American_av | Korean_av | Mediterranean_av | Thai_av | Asian Fusion_av | ... | Chinese_av_real | Japanese_av_real | Mexican_av_real | Italian_av_real | Others_av_real | American_av_real | Korean_av_real | Mediterranean_av_real | Thai_av_real | Asian Fusion_av_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | |||||||||||||||||||||
| ---1lKK3aKOuomHnwAkAow | NaN | 2.333333 | 3.666667 | 3.166667 | 4.352941 | 3.9375 | 4.0 | NaN | 3.25 | 3.5 | ... | NaN | 2.035809 | 3.720609 | 3.226154 | 4.524134 | 3.963953 | 4.0 | NaN | 3.134858 | 3.443446 |
| ---PLwSf5gKdIoVnyRHgBA | NaN | NaN | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | NaN |
| ---cu1hq55BP9DWVXXKHZg | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | ... | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 |
| ---udAKDsn0yQXmzbWQNSw | NaN | NaN | NaN | NaN | 5.000000 | NaN | NaN | NaN | 4.00 | NaN | ... | NaN | NaN | NaN | NaN | 5.000000 | NaN | NaN | NaN | 4.000000 | NaN |
| --0RtXvcOIE4XbErYca6Rw | NaN | NaN | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | 4.000000 | NaN | NaN | NaN | NaN | NaN |
5 rows × 30 columns
# split grouped_reviews and users datasets into n_cores parts, where n_cores is the number of available processors
n_cores = _os.cpu_count()
df_out = _np.array_split(users, n_cores) # list of input dataframes (from users)
df_out_names = [] # list of paths of output dataframes (from grouped_reviews)
df_in = []
for i, df in enumerate(df_out):
name = "../dataset/m2_n9/tmp/df_out_" + str(i) + ".pickle"
df_out_names += [name]
df_tmp = grouped_reviews.loc[df.index]
df_in += [df_tmp]
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\ipykernel_launcher.py:12: FutureWarning: Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative. See the documentation here: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike if sys.path[0] == '':
from multiproc_utils import user_business_features
if __name__ == '__main__':
with _Pool(processes=n_cores) as p:
p.map(user_business_features, zip(df_in, df_out, df_out_names))
users_chunks = []
# add chunks produced by subprocesses
for name in df_out_names:
df_out_i = _pd.read_pickle(name)
users_chunks += [df_out_i]
_os.remove(name)
users = _pd.concat(users_chunks)
users.head()
| user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | ... | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | |||||||||||||||||||||
| l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | ... | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | ... | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | ... | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | ... | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
5 rows × 45 columns
users = users.reset_index()
users.to_pickle('../dataset/m2_n9/users_2.pickle')
users.shape
(1148098, 46)
users_pre = _pd.read_pickle("../dataset/m2_n9/users.pickle")
users_pre.shape
(1148098, 16)
len(grouped_reviews)
954447
print("expected diff:", users.shape[0]-len(grouped_reviews))
expected diff: 193651
users_tmp = users[['av_rat_chinese_cuisine', 'av_rat_japanese_cuisine', 'av_rat_mexican_cuisine', 'av_rat_italian_cuisine',
'av_rat_others_cuisine', 'av_rat_american_cuisine', 'av_rat_korean_cuisine', 'av_rat_mediterranean_cuisine',
'av_rat_thai_cuisine', 'av_rat_asianfusion_cuisine',
'av_rat_chinese_cuisine_bin', 'av_rat_japanese_cuisine_bin', 'av_rat_mexican_cuisine_bin',
'av_rat_italian_cuisine_bin', 'av_rat_others_cuisine_bin', 'av_rat_american_cuisine_bin',
'av_rat_korean_cuisine_bin', 'av_rat_mediterranean_cuisine_bin', 'av_rat_thai_cuisine_bin',
'av_rat_asianfusion_cuisine_bin',
'av_rat_chinese_cuisine_real', 'av_rat_japanese_cuisine_real', 'av_rat_mexican_cuisine_real',
'av_rat_italian_cuisine_real', 'av_rat_others_cuisine_real', 'av_rat_american_cuisine_real',
'av_rat_korean_cuisine_real', 'av_rat_mediterranean_cuisine_real', 'av_rat_thai_cuisine_real',
'av_rat_asianfusion_cuisine_real']]
count_na = 0
for i, r in users_tmp.iterrows():
if r.isna().all():
count_na += 1
print("actual diff:", count_na)
actual diff: 193651
_del_all()
review_test = _pd.read_pickle('../dataset/m2_n9/review_test.pickle')
review_test = review_test.sort_values(by=['review_id'])
review_test = review_test.reset_index(drop = True)
review_test.shape
(153993, 10)
review_test.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---j05qHS2X7FkXjjMKKtA | E6Aoz-3s4avfweIjziHjbA | cTbFJzHQzFSX-z3JF4abKQ | 5 | 1 | 0 | 1 | 2018-11-02 15:01:11 | -1 | 0.229833 |
| 1 | --4GjusuUCMh24c_oh_cEg | YOOsYiXGEtGFX_wSeZNcww | JytR7WvKyytDQNwOHUzSEg | 4 | 1 | 0 | 0 | 2018-10-13 00:07:17 | 1 | 0.987487 |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | Bdw4E8jFVd6-CbhrNAJ_EA | -CfFjcCcGGDM9MVH_d42RQ | 5 | 0 | 0 | 0 | 2018-10-14 04:41:42 | -1 | 0.524751 |
| 3 | --4vJzoC0m5h-yodXv-qCw | jm4a1GghQ4zLCN3lQGMQUQ | XMPBg6r_LqZhy9Cf-4ZJrA | 2 | 0 | 0 | 0 | 2018-11-01 19:11:50 | 1 | 0.979875 |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | x_6VdQU3CIdakwHod-dNzA | Fn_IxcCtZl1EoS81sq_s9w | 3 | 1 | 0 | 0 | 2018-10-31 02:01:04 | 1 | 0.662471 |
restaurants = _pd.read_pickle('../dataset/m2_n9/restaurants.pickle')
restaurants = restaurants.reset_index(drop = True)
restaurants = restaurants[['cuisine', 'business_id']]
restaurants.head()
| cuisine | business_id | |
|---|---|---|
| 0 | Chinese | QXAEGFB4oINsVuTFxEYKFQ |
| 1 | Japanese | gnKjwL_1w79qoiV3IC_xQQ |
| 2 | Mexican | 1Dfx3zM-rW4n-31KeC8sJg |
| 3 | Italian | fweCYi8FmbJXHCqLnwuk8w |
| 4 | Italian | PZ-LZzSlhSe9utkQYU8pFg |
review_test_rest = review_test.join(restaurants.set_index('business_id'), on = 'business_id')
review_test_rest.to_pickle('../dataset/m2_n9/review_test_cuisine.pickle')
review_test_rest.shape
(153993, 11)
review_test_rest.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---j05qHS2X7FkXjjMKKtA | E6Aoz-3s4avfweIjziHjbA | cTbFJzHQzFSX-z3JF4abKQ | 5 | 1 | 0 | 1 | 2018-11-02 15:01:11 | -1 | 0.229833 | American |
| 1 | --4GjusuUCMh24c_oh_cEg | YOOsYiXGEtGFX_wSeZNcww | JytR7WvKyytDQNwOHUzSEg | 4 | 1 | 0 | 0 | 2018-10-13 00:07:17 | 1 | 0.987487 | Others |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | Bdw4E8jFVd6-CbhrNAJ_EA | -CfFjcCcGGDM9MVH_d42RQ | 5 | 0 | 0 | 0 | 2018-10-14 04:41:42 | -1 | 0.524751 | Mediterranean |
| 3 | --4vJzoC0m5h-yodXv-qCw | jm4a1GghQ4zLCN3lQGMQUQ | XMPBg6r_LqZhy9Cf-4ZJrA | 2 | 0 | 0 | 0 | 2018-11-01 19:11:50 | 1 | 0.979875 | Others |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | x_6VdQU3CIdakwHod-dNzA | Fn_IxcCtZl1EoS81sq_s9w | 3 | 1 | 0 | 0 | 2018-10-31 02:01:04 | 1 | 0.662471 | Italian |
del restaurants
users = _pd.read_pickle('../dataset/m2_n9/users_2.pickle')
users.head()
| user_id | user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | 2.0 | 3.000000 | 2.0 | 3.017247 | 1.957334 | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 1 | 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | 12.0 | 3.777778 | 9.0 | 3.626019 | 9.280533 | NaN | NaN | NaN | NaN | 4.0 | 3.333333 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.666667 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| 2 | bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | 13.0 | 3.500000 | 12.0 | 3.326528 | 10.350275 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.000000 | NaN | 3.0 | 3.666667 | 2.0 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.500000 | NaN | 3.0 | 3.666667 | 2.0 | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | 1.0 | NaN | 0.0 | 5.000000 | 0.329341 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | 5.0 | 4.400000 | 5.0 | 4.378799 | 4.720870 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
users = users[['user_id', 'av_rat_chinese_cuisine', 'av_rat_japanese_cuisine', 'av_rat_mexican_cuisine', 'av_rat_italian_cuisine',
'av_rat_others_cuisine', 'av_rat_american_cuisine', 'av_rat_korean_cuisine', 'av_rat_mediterranean_cuisine',
'av_rat_thai_cuisine', 'av_rat_asianfusion_cuisine',
'av_rat_chinese_cuisine_bin', 'av_rat_japanese_cuisine_bin', 'av_rat_mexican_cuisine_bin',
'av_rat_italian_cuisine_bin', 'av_rat_others_cuisine_bin', 'av_rat_american_cuisine_bin',
'av_rat_korean_cuisine_bin', 'av_rat_mediterranean_cuisine_bin', 'av_rat_thai_cuisine_bin',
'av_rat_asianfusion_cuisine_bin',
'av_rat_chinese_cuisine_real', 'av_rat_japanese_cuisine_real', 'av_rat_mexican_cuisine_real',
'av_rat_italian_cuisine_real', 'av_rat_others_cuisine_real', 'av_rat_american_cuisine_real',
'av_rat_korean_cuisine_real', 'av_rat_mediterranean_cuisine_real', 'av_rat_thai_cuisine_real',
'av_rat_asianfusion_cuisine_real']]
users.head()
| user_id | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 1 | 4XChL029mKr5hydo79Ljxg | NaN | NaN | NaN | NaN | 4.0 | 3.333333 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.666667 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| 2 | bc8C_eETBWL0olvFSJJd0w | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.000000 | NaN | 3.0 | 3.666667 | 2.0 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.500000 | NaN | 3.0 | 3.666667 | 2.0 | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
test_join = review_test_rest.join(users.set_index('user_id'), on = 'user_id', lsuffix = '_test_revirew', rsuffix = '_users')
test_join.shape
(153993, 41)
test_join.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---j05qHS2X7FkXjjMKKtA | E6Aoz-3s4avfweIjziHjbA | cTbFJzHQzFSX-z3JF4abKQ | 5 | 1 | 0 | 1 | 2018-11-02 15:01:11 | -1 | 0.229833 | American | NaN | 4.00 | NaN | 4.500 | 3.444444 | 4.000000 | NaN | NaN | NaN | 4.0 | NaN | 4.00 | NaN | 4.500 | 3.444444 | 4.285714 | NaN | NaN | NaN | 4.0 | NaN | 4.000000 | NaN | 4.508384 | 3.446572 | 3.969038 | NaN | NaN | NaN | 4.102936 |
| 1 | --4GjusuUCMh24c_oh_cEg | YOOsYiXGEtGFX_wSeZNcww | JytR7WvKyytDQNwOHUzSEg | 4 | 1 | 0 | 0 | 2018-10-13 00:07:17 | 1 | 0.987487 | Others | 3.75 | 3.75 | 4.0 | 3.625 | 3.604167 | 3.230769 | 2.0 | 4.4 | 3.666667 | 3.0 | 3.75 | 3.75 | 4.666667 | 3.625 | 3.607143 | 3.333333 | 2.0 | 4.25 | 3.666667 | 3.0 | 3.756843 | 3.726028 | 4.287215 | 3.525072 | 3.610109 | 3.170261 | 2.0 | 4.355007 | 3.664197 | 3.021022 |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | Bdw4E8jFVd6-CbhrNAJ_EA | -CfFjcCcGGDM9MVH_d42RQ | 5 | 0 | 0 | 0 | 2018-10-14 04:41:42 | -1 | 0.524751 | Mediterranean | NaN | NaN | 1.0 | NaN | 5.000000 | 5.000000 | NaN | 5.0 | NaN | NaN | NaN | NaN | 1.000000 | NaN | 5.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 | NaN | 5.000000 | 5.000000 | NaN | 5.000000 | NaN | NaN |
| 3 | --4vJzoC0m5h-yodXv-qCw | jm4a1GghQ4zLCN3lQGMQUQ | XMPBg6r_LqZhy9Cf-4ZJrA | 2 | 0 | 0 | 0 | 2018-11-01 19:11:50 | 1 | 0.979875 | Others | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | x_6VdQU3CIdakwHod-dNzA | Fn_IxcCtZl1EoS81sq_s9w | 3 | 1 | 0 | 0 | 2018-10-31 02:01:04 | 1 | 0.662471 | Italian | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
test_join.to_pickle('../dataset/m2_n9/join_test_users_review.pickle')
del users, review_test_rest
def _restaturants_users_cuisine_ratings(grouped):
cuisines = str(grouped['cuisine']).split(", ")
d = {'review_id' : grouped['review_id'],'cuisine_av_hist' : 0, 'cuisine_av_hist_bin' : 0, 'cuisine_av_hist_real': 0}
index = ['review_id', 'cuisine_av_hist', 'cuisine_av_hist_bin', 'cuisine_av_hist_real']
values = []
for cuisine in cuisines:
cui = cuisine.lower().replace(" ", "")
name = "av_rat_{0}_cuisine".format(cui)
values.append(grouped[name])
d['cuisine_av_hist'] = _np.average(values)
values = []
for cuisine in cuisines:
cui = cuisine.lower().replace(" ", "")
name = "av_rat_{0}_cuisine_bin".format(cui)
values.append(grouped[name])
d['cuisine_av_hist_bin'] = _np.average(values)
values = []
for cuisine in cuisines:
cui = cuisine.lower().replace(" ", "")
name = "av_rat_{0}_cuisine_real".format(cui)
values.append(grouped[name])
d['cuisine_av_hist_real'] = _np.average(values)
return _pd.Series(d, index = index)
applied_test = test_join.apply(_restaturants_users_cuisine_ratings, axis = 1)
applied_test.shape
(153993, 4)
applied_test = applied_test.sort_values(by=['review_id'])
applied_test = applied_test.reset_index(drop = True)
applied_test.head()
| review_id | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | |
|---|---|---|---|---|
| 0 | ---j05qHS2X7FkXjjMKKtA | 4.000000 | 4.285714 | 3.969038 |
| 1 | --4GjusuUCMh24c_oh_cEg | 3.604167 | 3.607143 | 3.610109 |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | 5.000000 | NaN | 5.000000 |
| 3 | --4vJzoC0m5h-yodXv-qCw | NaN | NaN | NaN |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | NaN | NaN | NaN |
applied_test.to_pickle('../dataset/m2_n9/applied_test_users_review.pickle')
review_test.shape
(153993, 10)
review_test.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---j05qHS2X7FkXjjMKKtA | E6Aoz-3s4avfweIjziHjbA | cTbFJzHQzFSX-z3JF4abKQ | 5 | 1 | 0 | 1 | 2018-11-02 15:01:11 | -1 | 0.229833 |
| 1 | --4GjusuUCMh24c_oh_cEg | YOOsYiXGEtGFX_wSeZNcww | JytR7WvKyytDQNwOHUzSEg | 4 | 1 | 0 | 0 | 2018-10-13 00:07:17 | 1 | 0.987487 |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | Bdw4E8jFVd6-CbhrNAJ_EA | -CfFjcCcGGDM9MVH_d42RQ | 5 | 0 | 0 | 0 | 2018-10-14 04:41:42 | -1 | 0.524751 |
| 3 | --4vJzoC0m5h-yodXv-qCw | jm4a1GghQ4zLCN3lQGMQUQ | XMPBg6r_LqZhy9Cf-4ZJrA | 2 | 0 | 0 | 0 | 2018-11-01 19:11:50 | 1 | 0.979875 |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | x_6VdQU3CIdakwHod-dNzA | Fn_IxcCtZl1EoS81sq_s9w | 3 | 1 | 0 | 0 | 2018-10-31 02:01:04 | 1 | 0.662471 |
review_test = review_test.assign(cuisine_av_hist = applied_test['cuisine_av_hist'],
cuisine_av_hist_bin = applied_test['cuisine_av_hist_bin'],
cuisine_av_hist_real = applied_test['cuisine_av_hist_real'])
review_test.shape
(153993, 13)
review_test.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---j05qHS2X7FkXjjMKKtA | E6Aoz-3s4avfweIjziHjbA | cTbFJzHQzFSX-z3JF4abKQ | 5 | 1 | 0 | 1 | 2018-11-02 15:01:11 | -1 | 0.229833 | 4.000000 | 4.285714 | 3.969038 |
| 1 | --4GjusuUCMh24c_oh_cEg | YOOsYiXGEtGFX_wSeZNcww | JytR7WvKyytDQNwOHUzSEg | 4 | 1 | 0 | 0 | 2018-10-13 00:07:17 | 1 | 0.987487 | 3.604167 | 3.607143 | 3.610109 |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | Bdw4E8jFVd6-CbhrNAJ_EA | -CfFjcCcGGDM9MVH_d42RQ | 5 | 0 | 0 | 0 | 2018-10-14 04:41:42 | -1 | 0.524751 | 5.000000 | NaN | 5.000000 |
| 3 | --4vJzoC0m5h-yodXv-qCw | jm4a1GghQ4zLCN3lQGMQUQ | XMPBg6r_LqZhy9Cf-4ZJrA | 2 | 0 | 0 | 0 | 2018-11-01 19:11:50 | 1 | 0.979875 | NaN | NaN | NaN |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | x_6VdQU3CIdakwHod-dNzA | Fn_IxcCtZl1EoS81sq_s9w | 3 | 1 | 0 | 0 | 2018-10-31 02:01:04 | 1 | 0.662471 | NaN | NaN | NaN |
test_set = review_test
test_set.to_pickle('../dataset/m2_n9/review_test_cuisine_final.pickle')
_del_all()
review_train = _pd.read_pickle('../dataset/m2_n9/review_train.pickle')
review_train = review_train.sort_values(by=['review_id'])
review_train = review_train.reset_index(drop = True)
review_train.shape
(558386, 10)
review_train.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---HLAnHbuLi7vd5TL6uYg | zyp8SaRnZ94sWZpLrifS1Q | l6xZVTEtdZAvNpL1JhYGuw | 4 | 0 | 0 | 0 | 2018-05-26 13:49:33 | -1 | 0.595406 |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 2018-04-15 18:54:27 | 1 | 0.993384 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 2018-07-07 15:16:09 | 1 | 0.519254 |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 2018-05-09 13:59:37 | 1 | 0.983368 |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 2018-06-16 17:00:42 | 1 | 0.866956 |
restaurants = _pd.read_pickle('../dataset/m2_n9/restaurants.pickle')
restaurants = restaurants.reset_index(drop = True)
restaurants = restaurants[['cuisine', 'business_id']]
restaurants.head()
| cuisine | business_id | |
|---|---|---|
| 0 | Chinese | QXAEGFB4oINsVuTFxEYKFQ |
| 1 | Japanese | gnKjwL_1w79qoiV3IC_xQQ |
| 2 | Mexican | 1Dfx3zM-rW4n-31KeC8sJg |
| 3 | Italian | fweCYi8FmbJXHCqLnwuk8w |
| 4 | Italian | PZ-LZzSlhSe9utkQYU8pFg |
review_train_rest = review_train.join(restaurants.set_index('business_id'), on = 'business_id')
review_train_rest.to_pickle('../dataset/m2_n9/review_train_cuisine.pickle')
review_train_rest.shape
(558386, 11)
review_train_rest.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---HLAnHbuLi7vd5TL6uYg | zyp8SaRnZ94sWZpLrifS1Q | l6xZVTEtdZAvNpL1JhYGuw | 4 | 0 | 0 | 0 | 2018-05-26 13:49:33 | -1 | 0.595406 | Italian, Mediterranean |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 2018-04-15 18:54:27 | 1 | 0.993384 | Others |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 2018-07-07 15:16:09 | 1 | 0.519254 | American |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 2018-05-09 13:59:37 | 1 | 0.983368 | Others |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 2018-06-16 17:00:42 | 1 | 0.866956 | American |
del restaurants
users = _pd.read_pickle('../dataset/m2_n9/users_2.pickle')
users.head()
| user_id | user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | 2.0 | 3.000000 | 2.0 | 3.017247 | 1.957334 | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 1 | 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | 12.0 | 3.777778 | 9.0 | 3.626019 | 9.280533 | NaN | NaN | NaN | NaN | 4.0 | 3.333333 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.666667 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| 2 | bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | 13.0 | 3.500000 | 12.0 | 3.326528 | 10.350275 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.000000 | NaN | 3.0 | 3.666667 | 2.0 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.500000 | NaN | 3.0 | 3.666667 | 2.0 | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | 1.0 | NaN | 0.0 | 5.000000 | 0.329341 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | 5.0 | 4.400000 | 5.0 | 4.378799 | 4.720870 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
users = users[['user_id', 'av_rat_chinese_cuisine', 'av_rat_japanese_cuisine', 'av_rat_mexican_cuisine', 'av_rat_italian_cuisine',
'av_rat_others_cuisine', 'av_rat_american_cuisine', 'av_rat_korean_cuisine', 'av_rat_mediterranean_cuisine',
'av_rat_thai_cuisine', 'av_rat_asianfusion_cuisine',
'av_rat_chinese_cuisine_bin', 'av_rat_japanese_cuisine_bin', 'av_rat_mexican_cuisine_bin',
'av_rat_italian_cuisine_bin', 'av_rat_others_cuisine_bin', 'av_rat_american_cuisine_bin',
'av_rat_korean_cuisine_bin', 'av_rat_mediterranean_cuisine_bin', 'av_rat_thai_cuisine_bin',
'av_rat_asianfusion_cuisine_bin',
'av_rat_chinese_cuisine_real', 'av_rat_japanese_cuisine_real', 'av_rat_mexican_cuisine_real',
'av_rat_italian_cuisine_real', 'av_rat_others_cuisine_real', 'av_rat_american_cuisine_real',
'av_rat_korean_cuisine_real', 'av_rat_mediterranean_cuisine_real', 'av_rat_thai_cuisine_real',
'av_rat_asianfusion_cuisine_real']]
users.head()
| user_id | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 1 | 4XChL029mKr5hydo79Ljxg | NaN | NaN | NaN | NaN | 4.0 | 3.333333 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.666667 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| 2 | bc8C_eETBWL0olvFSJJd0w | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.000000 | NaN | 3.0 | 3.666667 | 2.0 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.500000 | NaN | 3.0 | 3.666667 | 2.0 | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
train_join = review_train_rest.join(users.set_index('user_id'), on = 'user_id', lsuffix = '_train_revirew', rsuffix = '_users')
train_join.shape
(558386, 41)
train_join.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---HLAnHbuLi7vd5TL6uYg | zyp8SaRnZ94sWZpLrifS1Q | l6xZVTEtdZAvNpL1JhYGuw | 4 | 0 | 0 | 0 | 2018-05-26 13:49:33 | -1 | 0.595406 | Italian, Mediterranean | NaN | NaN | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.022921 | NaN | NaN | NaN | NaN | NaN |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 2018-04-15 18:54:27 | 1 | 0.993384 | Others | 3.833333 | 2.875 | 4.0 | 3.25 | 3.013889 | 3.125 | 3.333333 | 3.4 | 2.666667 | 2.666667 | 3.823529 | 2.6 | 4.0 | 3.25 | 3.014925 | 3.066667 | 3.333333 | 3.4 | 2.4 | 2.666667 | 3.821347 | 2.726907 | 4.0 | 3.303025 | 3.039444 | 3.145065 | 2.847918 | 3.387395 | 2.572254 | 2.752899 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 2018-07-07 15:16:09 | 1 | 0.519254 | American | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 2018-05-09 13:59:37 | 1 | 0.983368 | Others | NaN | NaN | NaN | 1.00 | 1.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | NaN | NaN | NaN | NaN | NaN |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 2018-06-16 17:00:42 | 1 | 0.866956 | American | NaN | NaN | 4.0 | 4.50 | 5.000000 | 2.750 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 4.50 | 5.000000 | 2.666667 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 4.507847 | 5.000000 | 2.748879 | NaN | NaN | NaN | NaN |
train_join.to_pickle('../dataset/m2_n9/join_train_users_review.pickle')
del users, review_train_rest
applied_train = train_join.apply(_restaturants_users_cuisine_ratings, axis = 1)
applied_train.shape
(558386, 4)
applied_train = applied_train.sort_values(by=['review_id'])
applied_train = applied_train.reset_index(drop = True)
applied_train.head()
| review_id | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | |
|---|---|---|---|---|
| 0 | ---HLAnHbuLi7vd5TL6uYg | NaN | NaN | NaN |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 3.013889 | 3.014925 | 3.039444 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | NaN | NaN | NaN |
| 3 | --0SzSMXVUoAXfackNoB4g | 1.000000 | 1.000000 | 1.000000 |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2.750000 | 2.666667 | 2.748879 |
applied_train.to_pickle('../dataset/m2_n9/applied_train_users_review.pickle')
review_train.shape
(558386, 10)
review_train.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---HLAnHbuLi7vd5TL6uYg | zyp8SaRnZ94sWZpLrifS1Q | l6xZVTEtdZAvNpL1JhYGuw | 4 | 0 | 0 | 0 | 2018-05-26 13:49:33 | -1 | 0.595406 |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 2018-04-15 18:54:27 | 1 | 0.993384 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 2018-07-07 15:16:09 | 1 | 0.519254 |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 2018-05-09 13:59:37 | 1 | 0.983368 |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 2018-06-16 17:00:42 | 1 | 0.866956 |
review_train = review_train.assign(cuisine_av_hist = applied_train['cuisine_av_hist'],
cuisine_av_hist_bin = applied_train['cuisine_av_hist_bin'],
cuisine_av_hist_real = applied_train['cuisine_av_hist_real'])
review_train.shape
(558386, 13)
review_train.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ---HLAnHbuLi7vd5TL6uYg | zyp8SaRnZ94sWZpLrifS1Q | l6xZVTEtdZAvNpL1JhYGuw | 4 | 0 | 0 | 0 | 2018-05-26 13:49:33 | -1 | 0.595406 | NaN | NaN | NaN |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 2018-04-15 18:54:27 | 1 | 0.993384 | 3.013889 | 3.014925 | 3.039444 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 2018-07-07 15:16:09 | 1 | 0.519254 | NaN | NaN | NaN |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 2018-05-09 13:59:37 | 1 | 0.983368 | 1.000000 | 1.000000 | 1.000000 |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 2018-06-16 17:00:42 | 1 | 0.866956 | 2.750000 | 2.666667 | 2.748879 |
train_set = review_train
train_set.to_pickle('../dataset/m2_n9/review_train_cuisine_final.pickle')
_del_all()
$pred(u, r) = a_u + \frac{\sum_{u_i \in U} sim(u, u_i) * a_{u_i, r} - a_r} {\sum_{u_i \in U} sim(u, u_i)}$
users = _pd.read_pickle('../dataset/m2_n9/users_2.pickle')
users.set_index('user_id', inplace=True)
users.shape
(1148098, 45)
from multiproc_utils import cols_std, cols_bin, cols_real
users = users[[*cols_std, *cols_bin, *cols_real]]
users.shape
(1148098, 30)
users = users.fillna(users.mean())
restaurants = _pd.read_pickle('../dataset/m2_n9/restaurants.pickle')
restaurants.set_index('business_id', inplace=True)
review_hist = _pd.read_pickle('../dataset/m2_n9/review_hist.pickle')
review_hist.shape
(3489305, 10)
review_train = _pd.read_pickle('../dataset/m2_n9/review_train_cuisine_final.pickle')
review_train.set_index('review_id', inplace=True)
review_train.sort_values('business_id', inplace=True)
review_train.shape
(558386, 12)
review_train = review_train.assign(coll_score=_np.nan, coll_score_bin=_np.nan, coll_score_real=_np.nan)
review_train.shape
(558386, 15)
# split review_train, review_hist, restaurants and users datasets into n_cores parts, where n_cores is the number of available processors
n_cores = _os.cpu_count()
review_train_splits = _np.array_split(review_train, n_cores) # list of input dataframes (from review_train)
review_split_names = [] # list of paths of review_train dataframes
user_splits = []
restaurant_splits = []
review_hist_splits = []
for i, df in enumerate(review_train_splits):
name = "../dataset/m2_n9/tmp/review_train_split_" + str(i) + ".pickle"
review_split_names += [name]
this_review_hist = review_hist.loc[review_hist.business_id.isin(review_hist.business_id.unique())]
review_hist_splits += [this_review_hist]
user_ids = set(df.user_id) | set(this_review_hist.user_id)
user_splits += [users.loc[user_ids]]
restaurant_splits += [restaurants.loc[df.business_id.unique()]]
from multiproc_utils import set_coll_scores
if __name__ == '__main__':
with _Pool(processes=n_cores) as p:
p.map(set_coll_scores, zip(review_train_splits, review_split_names, review_hist_splits, user_splits, restaurant_splits))
review_chunks = []
# add chunks produced by subprocesses
for name in review_split_names:
review_chunks += [_pd.read_pickle(name)]
_os.remove(name)
review_train = _pd.concat(review_chunks)
review_train.shape
(558386, 15)
review_train.reset_index(inplace=True)
review_train.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 |
review_train.to_pickle('../dataset/m2_n9/review_train.pickle')
del review_train
review_test = _pd.read_pickle('../dataset/m2_n9/review_test_cuisine_final.pickle')
review_test.set_index('review_id', inplace=True)
review_test.shape
(153993, 12)
review_test = review_test.assign(coll_score=_np.nan, coll_score_bin=_np.nan, coll_score_real=_np.nan)
review_test.shape
(153993, 15)
# split review_test, review_hist, restaurants and users datasets into n_cores parts, where n_cores is the number of available processors
review_test.sort_values('business_id', inplace=True)
review_test_splits = _np.array_split(review_test, n_cores) # list of input dataframes (from review_test)
review_split_names = [] # list of paths of review_test dataframes
user_splits = []
restaurant_splits = []
review_hist_splits = []
for i, df in enumerate(review_test_splits):
name = "../dataset/m2_n9/tmp/review_test_split_" + str(i) + ".pickle"
review_split_names += [name]
this_review_hist = review_hist.loc[review_hist.business_id.isin(review_hist.business_id.unique())]
review_hist_splits += [this_review_hist]
user_ids = set(df.user_id) | set(this_review_hist.user_id)
user_splits += [users.loc[user_ids]]
restaurant_splits += [restaurants.loc[df.business_id.unique()]]
from multiproc_utils import set_coll_scores
if __name__ == '__main__':
with _Pool(processes=n_cores) as p:
p.map(set_coll_scores, zip(review_test_splits, review_split_names, review_hist_splits, user_splits, restaurant_splits))
review_chunks = []
# add chunks produced by subprocesses
for name in review_split_names:
review_chunks += [_pd.read_pickle(name)]
_os.remove(name)
review_test = _pd.concat(review_chunks)
review_test.shape
(153993, 15)
review_test.reset_index(inplace=True)
review_test.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN |
review_test.to_pickle('../dataset/m2_n9/review_test.pickle')
_del_all()
review_train = _pd.read_pickle('../dataset/m2_n9/review_train.pickle')
review_train = review_train.assign(likes = _np.nan)
review_train['likes'] = _np.where(review_train['stars'].isin([4, 5]), 1, 0)
review_train.head(10)
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN | 1 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 |
| 5 | rPNt-m2pdt-OR_NMQnHjCQ | tKIihU81IA3NjpsADuR-Tg | --6MefnULPED_I942VcFNA | 5 | 3 | 0 | 2 | 2018-07-08 19:29:51 | 1 | 0.907921 | 4.400000 | 4.400000 | 4.395327 | 4.376569 | 4.405801 | 4.313458 | 1 |
| 6 | Kg582pH05mZO_E6WS8PrKA | XNOs3Wz1Q_zdRgm1Hy05fg | --6MefnULPED_I942VcFNA | 1 | 2 | 2 | 1 | 2018-08-14 21:59:34 | -1 | 0.184327 | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 7 | gkW6_UqV9b2XI_5ae8rBCg | HSHuSCJvIvf_Tof62uZPEw | --6MefnULPED_I942VcFNA | 2 | 2 | 1 | 0 | 2018-08-19 04:01:19 | -1 | 0.687126 | 1.800000 | 1.692308 | 1.768813 | 1.816353 | 1.733574 | 1.724494 | 0 |
| 8 | 02voOwsYf0cEdKNzt5IkwA | yvpX68yurPsope6KhBZrYA | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-02-19 03:29:10 | 1 | 0.935335 | NaN | NaN | NaN | NaN | NaN | NaN | 1 |
| 9 | M67I-I5ATaqtVLtKZTgygw | gvh8bvei5vwfoIYbNIvNDQ | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-06-24 18:41:08 | -1 | 0.242671 | 3.000000 | 3.000000 | 3.040677 | 3.007972 | 3.009842 | 3.062459 | 1 |
restaurants = _pd.read_pickle('../dataset/m2_n9/restaurants.pickle')
restaurants = restaurants.reset_index(drop = True)
restaurants.head()
| business_id | name | address | cuisine | postal_code | latitude | longitude | review_count | stars | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | QXAEGFB4oINsVuTFxEYKFQ | Emerald Chinese Restaurant | 30 Eglinton Avenue W | Chinese | L5R 3E7 | 43.605499 | -79.652289 | 128 | 2.5 | False | NaN | False | True | No | Full_Bar | Specialty Food, Restaurants, Dim Sum, Imported... | Mississauga | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 2.726496 | 117.0 | 2.718750 | 96.0 | 2.730197 | 95.873087 |
| 1 | gnKjwL_1w79qoiV3IC_xQQ | Musashi Japanese Restaurant | 10110 Johnston Rd, Ste 15 | Japanese | 28210 | 35.092564 | -80.859132 | 170 | 4.0 | False | True | False | True | No | Beer&Wine | Sushi Bars, Restaurants, Japanese | Charlotte | 17:30:00 | NaT | 17:30:00 | 17:30:00 | 17:30:00 | 17:30:00 | 17:30:00 | 21:30:00 | NaT | 21:30:00 | 21:30:00 | 22:00:00 | 22:00:00 | 21:00:00 | 4.063291 | 158.0 | 4.094203 | 138.0 | 4.067541 | 139.112078 |
| 2 | 1Dfx3zM-rW4n-31KeC8sJg | Taco Bell | 2450 E Indian School Rd | Mexican | 85016 | 33.495194 | -112.028588 | 18 | 3.0 | False | True | False | False | No | No | Restaurants, Breakfast & Brunch, Mexican, Taco... | Phoenix | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 01:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 3.125000 | 16.0 | 2.769231 | 13.0 | 2.847327 | 12.604125 |
| 3 | fweCYi8FmbJXHCqLnwuk8w | Marco's Pizza | 5981 Andrews Rd | Italian | 44060 | 41.708520 | -81.359556 | 16 | 4.0 | False | True | True | False | NaN | No | Italian, Restaurants, Pizza, Chicken Wings | Mentor-on-the-Lake | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 4.230769 | 13.0 | 4.166667 | 12.0 | 4.142021 | 10.965903 |
| 4 | PZ-LZzSlhSe9utkQYU8pFg | Carluccio's Tivoli Gardens | 1775 E Tropicana Ave, Ste 29 | Italian | 89119 | 36.100016 | -115.128529 | 40 | 4.0 | False | True | False | True | No | Full_Bar | Restaurants, Italian | Las Vegas | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | 4.097561 | 41.0 | 4.212121 | 33.0 | 4.167159 | 33.655622 |
review_rest_train = review_train.join(restaurants.set_index('business_id'), on = 'business_id', lsuffix = '_review', rsuffix = '_restaurant')
review_rest_train.head()
| review_id | user_id | business_id | stars_review | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 |
print(len(review_train))
print(len(review_rest_train))
558386 558386
tips = _pd.read_pickle('../dataset/m2_n9/tips_train.pickle')
tips = tips.reset_index(drop = True)
tips.head()
| user_id | business_id | tips_date | compliment_count | |
|---|---|---|---|---|
| 0 | Fzz-0v1yHLaWuTV64b1miA | EZZjaiV8ik05NUepqdeP2A | 2018-02-14 | 0 |
| 1 | AuSB69SSnaPNTwwbFk93MQ | bAHDSbpJE3kKJkW9OBgOyw | 2018-03-23 | 0 |
| 2 | ouk36OGbx25nO23b10L5jw | THO77IL6DLob9Agt9QCjsw | 2018-03-22 | 0 |
| 3 | gwmyGLz4eBm9QiBU_Ze2KQ | RJOFGZZf3ho04ku0fcFRdA | 2018-03-29 | 0 |
| 4 | BzcdTNAe_jtXfnXFdFYQsA | _pBXtjN43eqMV0XZTz7nmw | 2018-04-02 | 0 |
tips_agg = tips.groupby(['business_id', 'user_id'])['compliment_count'].agg(_np.sum)
tips_agg.head()
business_id user_id
--6MefnULPED_I942VcFNA EisUuXVeVJN_FcFiE-tqwA 0
--7zmmkVg-IMGaXbuVd0SQ 9LlkZJ7NPsFSFMnIih8X1w 0
--9e1ONYQuAa-CB_Rrw7Tw 2J4PhasBxLtIv-kiS3_FiA 0
G2ZiNXL4rZdSxzaDSAAODQ 0
GFSZXppCJaO4oiqdgknWnA 0
Name: compliment_count, dtype: int64
review_tip_train = review_rest_train.join(tips_agg, on=['business_id', 'user_id'], lsuffix = '_review', rsuffix = '_tip')
review_tip_train.head()
| review_id | user_id | business_id | stars_review | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | compliment_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.0 |
print(len(review_train))
print(len(review_tip_train))
558386 558386
users = _pd.read_pickle('../dataset/m2_n9/users_2.pickle')
users = users.reset_index(drop = True)
users.head()
| user_id | user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | 2.0 | 3.000000 | 2.0 | 3.017247 | 1.957334 | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 1 | 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | 12.0 | 3.777778 | 9.0 | 3.626019 | 9.280533 | NaN | NaN | NaN | NaN | 4.0 | 3.333333 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.666667 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| 2 | bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | 13.0 | 3.500000 | 12.0 | 3.326528 | 10.350275 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.000000 | NaN | 3.0 | 3.666667 | 2.0 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.500000 | NaN | 3.0 | 3.666667 | 2.0 | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | 1.0 | NaN | 0.0 | 5.000000 | 0.329341 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | 5.0 | 4.400000 | 5.0 | 4.378799 | 4.720870 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
train_set = review_tip_train.join(users.set_index('user_id'), on = 'user_id', lsuffix = '_review', rsuffix = '_user')
del review_rest_train, users
train_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | user_name | average_stars_user | yelping_since | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Jexie | 3.846154 | 2009-06-24 | 18 | 0 | 1 | 8 | 4 | 4 | 4 | 13.0 | 4.222222 | 9.0 | 3.798692 | 11.168551 | NaN | 5.0 | NaN | NaN | 3.857143 | NaN | 3.0 | 5.0 | NaN | 2.00 | NaN | 5.0 | NaN | NaN | 5.000000 | NaN | 3.00 | 5.0 | NaN | 2.000000 | NaN | 5.0 | NaN | NaN | 3.926822 | NaN | 2.866472 | 5.000000 | NaN | 2.000000 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Alex | 3.333333 | 2015-07-27 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | NaN | NaN | 4.0 | NaN | 5.000000 | 2.5 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | NaN | 5.000000 | 2.5 | NaN | NaN | NaN | NaN | NaN | NaN | 4.000000 | NaN | 5.000000 | 2.353400 | NaN | NaN | NaN | NaN |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Mermaid | 2.571429 | 2012-01-01 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.0 | 3.0 | NaN | 3.200000 | NaN | NaN | 1.0 | 2.333333 | NaN | 2.500000 | 1.0 | 3.0 | NaN | 3.200000 | NaN | NaN | 1.0 | 3.0 | NaN | 2.505665 | 1.0 | 2.990709 | NaN | 3.204491 | NaN | NaN | 1.000000 | 2.974502 | NaN |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Jen | 3.703313 | 2013-02-27 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.0 | Alex | 2.980769 | 2008-11-15 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.0 | NaN | 2.0 | 3.040000 | 2.6 | 3.6 | 2.5 | 1.000000 | 3.75 | 2.923077 | 4.0 | NaN | 1.666667 | 3.146341 | 2.6 | 3.75 | 2.5 | 1.0 | 3.666667 | 2.954478 | 4.0 | NaN | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 |
print(len(review_train))
print(len(train_set))
558386 558386
train_set.to_pickle('../dataset/m2_n9/model_train_set.pickle')
_del_all()
review_test = _pd.read_pickle('../dataset/m2_n9/review_test.pickle')
review_test = review_test.assign(likes = _np.nan)
review_test['likes'] = _np.where(review_test['stars'].isin([4, 5]), 1, 0)
review_test.head()
| review_id | user_id | business_id | stars | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN | 1 |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN | 1 |
print(len(review_test))
153993
restaurants = _pd.read_pickle('../dataset/m2_n9/restaurants.pickle')
restaurants = restaurants.reset_index(drop = True)
restaurants.head()
| business_id | name | address | cuisine | postal_code | latitude | longitude | review_count | stars | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | QXAEGFB4oINsVuTFxEYKFQ | Emerald Chinese Restaurant | 30 Eglinton Avenue W | Chinese | L5R 3E7 | 43.605499 | -79.652289 | 128 | 2.5 | False | NaN | False | True | No | Full_Bar | Specialty Food, Restaurants, Dim Sum, Imported... | Mississauga | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 09:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 2.726496 | 117.0 | 2.718750 | 96.0 | 2.730197 | 95.873087 |
| 1 | gnKjwL_1w79qoiV3IC_xQQ | Musashi Japanese Restaurant | 10110 Johnston Rd, Ste 15 | Japanese | 28210 | 35.092564 | -80.859132 | 170 | 4.0 | False | True | False | True | No | Beer&Wine | Sushi Bars, Restaurants, Japanese | Charlotte | 17:30:00 | NaT | 17:30:00 | 17:30:00 | 17:30:00 | 17:30:00 | 17:30:00 | 21:30:00 | NaT | 21:30:00 | 21:30:00 | 22:00:00 | 22:00:00 | 21:00:00 | 4.063291 | 158.0 | 4.094203 | 138.0 | 4.067541 | 139.112078 |
| 2 | 1Dfx3zM-rW4n-31KeC8sJg | Taco Bell | 2450 E Indian School Rd | Mexican | 85016 | 33.495194 | -112.028588 | 18 | 3.0 | False | True | False | False | No | No | Restaurants, Breakfast & Brunch, Mexican, Taco... | Phoenix | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 07:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 01:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 3.125000 | 16.0 | 2.769231 | 13.0 | 2.847327 | 12.604125 |
| 3 | fweCYi8FmbJXHCqLnwuk8w | Marco's Pizza | 5981 Andrews Rd | Italian | 44060 | 41.708520 | -81.359556 | 16 | 4.0 | False | True | True | False | NaN | No | Italian, Restaurants, Pizza, Chicken Wings | Mentor-on-the-Lake | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 10:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 00:00:00 | 01:00:00 | 01:00:00 | 00:00:00 | 4.230769 | 13.0 | 4.166667 | 12.0 | 4.142021 | 10.965903 |
| 4 | PZ-LZzSlhSe9utkQYU8pFg | Carluccio's Tivoli Gardens | 1775 E Tropicana Ave, Ste 29 | Italian | 89119 | 36.100016 | -115.128529 | 40 | 4.0 | False | True | False | True | No | Full_Bar | Restaurants, Italian | Las Vegas | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | NaT | 4.097561 | 41.0 | 4.212121 | 33.0 | 4.167159 | 33.655622 |
review_rest_test = review_test.join(restaurants.set_index('business_id'), on = 'business_id', lsuffix = '_review', rsuffix = '_restaurant')
del restaurants
review_rest_test.head()
| review_id | user_id | business_id | stars_review | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | Delmonico Steakhouse | 3355 Las Vegas Blvd S | Others | 89109 | 36.123183 | -115.169190 | 1613 | 4.0 | False | True | False | True | No | Full_Bar | Cajun/Creole, Seafood, Steakhouses, Restaurants | Las Vegas | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:30:00 | 22:30:00 | 22:00:00 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 |
print(len(review_test))
print(len(review_rest_test))
153993 153993
tips = _pd.read_pickle('../dataset/m2_n9/tips_test.pickle')
tips = tips.reset_index(drop = True)
tips.head()
| user_id | business_id | tips_date | compliment_count | |
|---|---|---|---|---|
| 0 | Yr0B0aVb94i2oIm1gLXgfg | yGDiAVoQB8LX3OsJ4e2I0A | 2018-10-05 | 0 |
| 1 | z6qIyc-_oIbgDWAembZ04w | wFO5HLn-GSfYCSPPcbyqoA | 2018-10-12 | 0 |
| 2 | EyYVD9n7PlYYLTSEQ5t14w | yhDAzBBjFujZbHwBPfE2eQ | 2018-10-15 | 0 |
| 3 | 90fhUaWIY6ctVAX5jQ2GNQ | ZMJOURno1xJS7PG3ZhfeyQ | 2018-10-20 | 0 |
| 4 | k7J0CjxFoxdSayhlDE-k7w | X3qrbOkhdCjm0NTBX7T80Q | 2018-10-30 | 0 |
tips_agg = tips.groupby(['business_id', 'user_id'])['compliment_count'].agg(_np.sum)
tips_agg.head()
business_id user_id
--9e1ONYQuAa-CB_Rrw7Tw 4vj_0BQeXjCyNB7ESS5mGg 0
idlz5ohzqTX5NnOZrxzcsQ 0
x_Nu7oNHf4VHwqgV3qLpfg 0
-01XupAWZEXbdNbxNg5mEg 8bt-F30D_tW-ajPG-IpZSA 0
-0DET7VdEQOJVJ_v6klEug G-6X-llgA_qAxGxocykHzQ 0
Name: compliment_count, dtype: int64
review_tip_test = review_rest_test.join(tips_agg, on=['business_id', 'user_id'], lsuffix = '_review', rsuffix = '_tip')
review_tip_test.head()
| review_id | user_id | business_id | stars_review | useful | funny | cool | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | compliment_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | Delmonico Steakhouse | 3355 Las Vegas Blvd S | Others | 89109 | 36.123183 | -115.169190 | 1613 | 4.0 | False | True | False | True | No | Full_Bar | Cajun/Creole, Seafood, Steakhouses, Restaurants | Las Vegas | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:30:00 | 22:30:00 | 22:00:00 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | NaN |
print(len(review_test))
print(len(review_tip_test))
153993 153993
users = _pd.read_pickle('../dataset/m2_n9/users_2.pickle')
users = users.reset_index(drop = True)
users.head()
| user_id | user_name | average_stars | yelping_since | review | years_of_elite | fans | useful | cool | funny | friends | num_reviews | average_stars_bin | num_reviews_bin | average_stars_real | num_reviews_real | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | l6BmjZMeQD3rDxWUbiAiow | Rashmi | 3.000000 | 2013-10-08 | 95 | 3 | 5 | 84 | 25 | 17 | 2374 | 2.0 | 3.000000 | 2.0 | 3.017247 | 1.957334 | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 4.000000 | NaN | NaN | NaN | NaN |
| 1 | 4XChL029mKr5hydo79Ljxg | Jenna | 3.500000 | 2013-02-21 | 33 | 0 | 4 | 48 | 16 | 22 | 27646 | 12.0 | 3.777778 | 9.0 | 3.626019 | 9.280533 | NaN | NaN | NaN | NaN | 4.0 | 3.333333 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.666667 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 3.501823 | NaN | 4.0 | NaN | NaN |
| 2 | bc8C_eETBWL0olvFSJJd0w | David | 3.384615 | 2013-10-04 | 16 | 0 | 0 | 28 | 10 | 8 | 358 | 13.0 | 3.500000 | 12.0 | 3.326528 | 10.350275 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.000000 | NaN | 3.0 | 3.666667 | 2.0 | 3.5 | NaN | 2.0 | NaN | 5.0 | 3.500000 | NaN | 3.0 | 3.666667 | 2.0 | 3.399765 | NaN | 2.0 | NaN | 5.0 | 3.123598 | NaN | 3.0 | 3.632007 | 1.997225 |
| 3 | dD0gZpBctWGdWo9WlGuhlA | Angela | 5.000000 | 2014-05-22 | 17 | 0 | 5 | 30 | 14 | 4 | 12598 | 1.0 | NaN | 0.0 | 5.000000 | 0.329341 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 |
| 4 | MM4RJAeH6yuaN8oZDSt0RA | Nancy | 4.400000 | 2013-10-23 | 361 | 4 | 39 | 1114 | 665 | 279 | 5542 | 5.0 | 4.400000 | 5.0 | 4.378799 | 4.720870 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.500000 | NaN | 5.0 | 3.000000 | 5.0 | NaN | NaN | NaN | NaN | NaN | 4.450279 | NaN | 5.0 | 3.000000 | 5.000000 |
test_set = review_tip_test.join(users.set_index('user_id'), on = 'user_id', lsuffix = '_review', rsuffix = '_user')
del review_rest_test, users
test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | user_name | average_stars_user | yelping_since | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Fran | 3.703313 | 2017-01-09 | 67 | 1 | 1 | 16 | 6 | 2 | 3574 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Leung | 2.875000 | 2016-07-20 | 86 | 0 | 1 | 34 | 9 | 12 | 166 | 16.0 | 3.000000 | 15.0 | 2.909282 | 13.563981 | 1.8 | 2.20 | NaN | 4.5 | 3.333333 | 1.000000 | 3.0 | NaN | NaN | NaN | 2.000000 | 2.50 | NaN | 4.5 | 3.333333 | 1.0 | 3.0 | NaN | NaN | NaN | 1.799679 | 2.460018 | NaN | 4.571695 | 3.355656 | 1.000000 | 2.924220 | NaN | NaN | NaN |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Jo | 4.108108 | 2017-08-08 | 227 | 2 | 7 | 99 | 47 | 30 | 286 | 37.0 | 4.161290 | 31.0 | 4.130733 | 31.326167 | 4.3 | 3.75 | NaN | 3.5 | 4.454545 | 3.666667 | 3.8 | NaN | 4.0 | 4.0 | 4.333333 | 3.75 | NaN | 3.0 | 4.555556 | 3.5 | 4.0 | NaN | 4.0 | 4.0 | 4.299574 | 3.724926 | NaN | 3.340936 | 4.538601 | 3.626374 | 3.946442 | NaN | 4.0 | 4.0 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Cindy | 3.703313 | 2016-03-09 | 1 | 0 | 0 | 0 | 0 | 0 | 2110 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | Delmonico Steakhouse | 3355 Las Vegas Blvd S | Others | 89109 | 36.123183 | -115.169190 | 1613 | 4.0 | False | True | False | True | No | Full_Bar | Cajun/Creole, Seafood, Steakhouses, Restaurants | Las Vegas | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:30:00 | 22:30:00 | 22:00:00 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | NaN | Katie | 3.703313 | 2017-06-15 | 59 | 0 | 0 | 5 | 0 | 1 | 22 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
print(len(review_test))
print(len(test_set))
153993 153993
test_set.to_pickle('../dataset/m2_n9/model_test_set.pickle')
_del_all()
We have to fill missing values in the dataset, and then convert non-numerical features into numerical features, or drop them if they are not necessary for our models, so that the remaining features are readable by our models.
We execute two versions of this step, in order to obtain two different dataset: a complete one and a lighter one. The second one is used for grid search, that duplicates the input dataset to work in parallel and reduce the computation time to a reasonable amount.
We summarize what kind of data we have at the moment, in order to decide what to do with each feature.
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set.pickle')
train_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | user_name | average_stars_user | yelping_since | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Jexie | 3.846154 | 2009-06-24 | 18 | 0 | 1 | 8 | 4 | 4 | 4 | 13.0 | 4.222222 | 9.0 | 3.798692 | 11.168551 | NaN | 5.0 | NaN | NaN | 3.857143 | NaN | 3.0 | 5.0 | NaN | 2.00 | NaN | 5.0 | NaN | NaN | 5.000000 | NaN | 3.00 | 5.0 | NaN | 2.000000 | NaN | 5.0 | NaN | NaN | 3.926822 | NaN | 2.866472 | 5.000000 | NaN | 2.000000 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Alex | 3.333333 | 2015-07-27 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | NaN | NaN | 4.0 | NaN | 5.000000 | 2.5 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | NaN | 5.000000 | 2.5 | NaN | NaN | NaN | NaN | NaN | NaN | 4.000000 | NaN | 5.000000 | 2.353400 | NaN | NaN | NaN | NaN |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Mermaid | 2.571429 | 2012-01-01 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.0 | 3.0 | NaN | 3.200000 | NaN | NaN | 1.0 | 2.333333 | NaN | 2.500000 | 1.0 | 3.0 | NaN | 3.200000 | NaN | NaN | 1.0 | 3.0 | NaN | 2.505665 | 1.0 | 2.990709 | NaN | 3.204491 | NaN | NaN | 1.000000 | 2.974502 | NaN |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Jen | 3.703313 | 2013-02-27 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.0 | Alex | 2.980769 | 2008-11-15 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.0 | NaN | 2.0 | 3.040000 | 2.6 | 3.6 | 2.5 | 1.000000 | 3.75 | 2.923077 | 4.0 | NaN | 1.666667 | 3.146341 | 2.6 | 3.75 | 2.5 | 1.0 | 3.666667 | 2.954478 | 4.0 | NaN | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 |
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set.pickle')
test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | user_name | average_stars_user | yelping_since | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Fran | 3.703313 | 2017-01-09 | 67 | 1 | 1 | 16 | 6 | 2 | 3574 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Leung | 2.875000 | 2016-07-20 | 86 | 0 | 1 | 34 | 9 | 12 | 166 | 16.0 | 3.000000 | 15.0 | 2.909282 | 13.563981 | 1.8 | 2.20 | NaN | 4.5 | 3.333333 | 1.000000 | 3.0 | NaN | NaN | NaN | 2.000000 | 2.50 | NaN | 4.5 | 3.333333 | 1.0 | 3.0 | NaN | NaN | NaN | 1.799679 | 2.460018 | NaN | 4.571695 | 3.355656 | 1.000000 | 2.924220 | NaN | NaN | NaN |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Jo | 4.108108 | 2017-08-08 | 227 | 2 | 7 | 99 | 47 | 30 | 286 | 37.0 | 4.161290 | 31.0 | 4.130733 | 31.326167 | 4.3 | 3.75 | NaN | 3.5 | 4.454545 | 3.666667 | 3.8 | NaN | 4.0 | 4.0 | 4.333333 | 3.75 | NaN | 3.0 | 4.555556 | 3.5 | 4.0 | NaN | 4.0 | 4.0 | 4.299574 | 3.724926 | NaN | 3.340936 | 4.538601 | 3.626374 | 3.946442 | NaN | 4.0 | 4.0 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Cindy | 3.703313 | 2016-03-09 | 1 | 0 | 0 | 0 | 0 | 0 | 2110 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | Delmonico Steakhouse | 3355 Las Vegas Blvd S | Others | 89109 | 36.123183 | -115.169190 | 1613 | 4.0 | False | True | False | True | No | Full_Bar | Cajun/Creole, Seafood, Steakhouses, Restaurants | Las Vegas | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:30:00 | 22:30:00 | 22:00:00 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | NaN | Katie | 3.703313 | 2017-06-15 | 59 | 0 | 0 | 5 | 0 | 1 | 22 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
train_test_set = _pd.concat([train_set, test_set], sort=False)
print("train size:", train_set.shape)
print("test size:", test_set.shape)
print("train_test size:", train_test_set.shape)
print(train_set.shape[0] + test_set.shape[0] == train_test_set.shape[0])
_train_len = train_set.shape[0]
train size: (558386, 99) test size: (153993, 99) train_test size: (712379, 99) True
train_test_set.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 712379 entries, 0 to 153992 Data columns (total 99 columns): review_id 712379 non-null object user_id 712379 non-null object business_id 712379 non-null object stars_review 712379 non-null int64 useful_review 712379 non-null int64 funny_review 712379 non-null int64 cool_review 712379 non-null int64 date 712379 non-null datetime64[ns] bin_truth_score 712379 non-null int64 real_truth_score 712379 non-null float64 cuisine_av_hist 269090 non-null float64 cuisine_av_hist_bin 249642 non-null float64 cuisine_av_hist_real 269090 non-null float64 coll_score 269090 non-null float64 coll_score_bin 249642 non-null float64 coll_score_real 269090 non-null float64 likes 712379 non-null int32 name 712379 non-null object address 712379 non-null object cuisine 712379 non-null object postal_code 712379 non-null object latitude 712379 non-null float64 longitude 712379 non-null float64 review_count 712379 non-null int64 stars_restaurant 712379 non-null float64 OutdoorSeating 669576 non-null object BusinessAcceptsCreditCards 588620 non-null object RestaurantsDelivery 677719 non-null object RestaurantsReservations 680010 non-null object WiFi 658240 non-null object Alcohol 651883 non-null object categories 712379 non-null object city 712379 non-null object Monday_Open 645277 non-null object Tuesday_Open 668918 non-null object Wednesday_Open 679256 non-null object Thursday_Open 683039 non-null object Friday_Open 683398 non-null object Saturday_Open 679080 non-null object Sunday_Open 626609 non-null object Monday_Close 645277 non-null object Tuesday_Close 668918 non-null object Wednesday_Close 679256 non-null object Thursday_Close 683039 non-null object Friday_Close 683398 non-null object Saturday_Close 679080 non-null object Sunday_Close 626609 non-null object average_stars_review 712379 non-null float64 num_reviews_review 712379 non-null float64 average_stars_bin_review 711373 non-null float64 num_reviews_bin_review 712379 non-null float64 average_stars_real_review 712379 non-null float64 num_reviews_real_review 712379 non-null float64 compliment_count 17732 non-null float64 user_name 712379 non-null object average_stars_user 712379 non-null float64 yelping_since 712379 non-null object review 712379 non-null int64 years_of_elite 712379 non-null int64 fans 712379 non-null int64 useful_user 712379 non-null int64 cool_user 712379 non-null int64 funny_user 712379 non-null int64 friends 712379 non-null int64 num_reviews_user 712379 non-null float64 average_stars_bin_user 694363 non-null float64 num_reviews_bin_user 712379 non-null float64 average_stars_real_user 712379 non-null float64 num_reviews_real_user 712379 non-null float64 av_rat_chinese_cuisine 140210 non-null float64 av_rat_japanese_cuisine 149309 non-null float64 av_rat_mexican_cuisine 195524 non-null float64 av_rat_italian_cuisine 198832 non-null float64 av_rat_others_cuisine 305838 non-null float64 av_rat_american_cuisine 291851 non-null float64 av_rat_korean_cuisine 73297 non-null float64 av_rat_mediterranean_cuisine 122566 non-null float64 av_rat_thai_cuisine 105389 non-null float64 av_rat_asianfusion_cuisine 166768 non-null float64 av_rat_chinese_cuisine_bin 127182 non-null float64 av_rat_japanese_cuisine_bin 135034 non-null float64 av_rat_mexican_cuisine_bin 175440 non-null float64 av_rat_italian_cuisine_bin 180287 non-null float64 av_rat_others_cuisine_bin 286214 non-null float64 av_rat_american_cuisine_bin 272580 non-null float64 av_rat_korean_cuisine_bin 66395 non-null float64 av_rat_mediterranean_cuisine_bin 108226 non-null float64 av_rat_thai_cuisine_bin 94816 non-null float64 av_rat_asianfusion_cuisine_bin 151573 non-null float64 av_rat_chinese_cuisine_real 140210 non-null float64 av_rat_japanese_cuisine_real 149309 non-null float64 av_rat_mexican_cuisine_real 195524 non-null float64 av_rat_italian_cuisine_real 198832 non-null float64 av_rat_others_cuisine_real 305838 non-null float64 av_rat_american_cuisine_real 291851 non-null float64 av_rat_korean_cuisine_real 73297 non-null float64 av_rat_mediterranean_cuisine_real 122566 non-null float64 av_rat_thai_cuisine_real 105389 non-null float64 av_rat_asianfusion_cuisine_real 166768 non-null float64 dtypes: datetime64[ns](1), float64(53), int32(1), int64(13), object(31) memory usage: 540.8+ MB
train_test_types = train_test_set.dtypes
for ind, dtype in train_test_types.iteritems():
if not _np.issubdtype(dtype, _np.number):
if "id" not in ind:
uniq_vals = train_test_set[ind].unique()
null_vals = train_test_set[ind].isnull().sum()
print(ind + " - " + str(dtype) + " - unique: " + str(len(uniq_vals)) + " - nulls: " + str(null_vals))
print(uniq_vals[:10])
print()
date - datetime64[ns] - unique: 699960 - nulls: 0 ['2018-01-11T19:55:31.000000000' '2018-02-25T17:47:12.000000000' '2018-05-06T04:22:48.000000000' '2018-04-22T17:42:09.000000000' '2018-05-21T05:09:07.000000000' '2018-07-08T19:29:51.000000000' '2018-08-14T21:59:34.000000000' '2018-08-19T04:01:19.000000000' '2018-02-19T03:29:10.000000000' '2018-06-24T18:41:08.000000000'] name - object - unique: 26090 - nulls: 0 ['The Spicy Amigos' "John's Chinese BBQ Restaurant" 'Delmonico Steakhouse' 'Sunnyside Grill' 'The Bar At Bermuda & St. Rose' 'Mm Mm Pizza' 'Sushiya' 'Happy Moose Bar and Grill' "Denny's" 'Pio Pio'] address - object - unique: 35750 - nulls: 0 ['821 4 Avenue SW' '328 Highway 7 E, Chalmers Gate 11, Unit 10' '3355 Las Vegas Blvd S' '1218 Saint Clair Avenue W' '11624 Bermuda Rd' '407 S Central Ave' '1950 Chemin Fer Ã\xa0 Cheval' '9436 State Rte 14' '6207 Wilson Mills Rd' '1408 E Blvd'] cuisine - object - unique: 87 - nulls: 0 ['Mexican' 'Chinese' 'Others' 'American' 'Japanese' 'Italian' 'Asian Fusion' 'Korean' 'Mexican, American' 'Thai'] postal_code - object - unique: 8309 - nulls: 0 ['T2P 0K5' 'L4B 3P7' '89109' 'M6E' '89052' '15317' 'J3E 2T6' '44241' '44143' '28203'] OutdoorSeating - object - unique: 4 - nulls: 42803 ['True' 'False' nan 'None'] BusinessAcceptsCreditCards - object - unique: 4 - nulls: 123759 [nan 'True' 'False' 'None'] RestaurantsDelivery - object - unique: 4 - nulls: 34660 ['False' 'True' nan 'None'] RestaurantsReservations - object - unique: 4 - nulls: 32369 [nan 'True' 'False' 'None'] WiFi - object - unique: 4 - nulls: 54139 [nan 'No' 'Free' 'Paid'] Alcohol - object - unique: 4 - nulls: 60496 ['Beer&Wine' 'Full_Bar' 'No' nan] categories - object - unique: 22832 - nulls: 0 ['Restaurants, Mexican' 'Chinese, Restaurants' 'Cajun/Creole, Seafood, Steakhouses, Restaurants' 'Restaurants, Breakfast & Brunch' 'Nightlife, Beer, Wine & Spirits, Bars, Restaurants, American (New), Food' 'Restaurants, Pizza, Chicken Wings, Salad' 'Buffets, Restaurants, Japanese, Sushi Bars' 'Nightlife, Sports Bars, Restaurants, Bars, American (Traditional)' 'Breakfast & Brunch, American (Traditional), Restaurants, Diners' 'Restaurants, Spanish, Peruvian, Colombian, Latin American'] city - object - unique: 671 - nulls: 0 ['Calgary' 'Richmond Hill' 'Las Vegas' 'Toronto' 'Henderson' 'Canonsburg' 'Sainte-Julie' 'Streetsboro' 'Highland Heights' 'Charlotte'] Monday_Open - object - unique: 59 - nulls: 67102 [datetime.time(11, 0) datetime.time(17, 0) datetime.time(7, 0) datetime.time(0, 0) datetime.time(16, 0) NaT datetime.time(12, 0) datetime.time(10, 0) datetime.time(6, 0) datetime.time(6, 30)] Tuesday_Open - object - unique: 56 - nulls: 43461 [datetime.time(11, 0) datetime.time(17, 0) datetime.time(7, 0) datetime.time(0, 0) datetime.time(16, 0) NaT datetime.time(12, 0) datetime.time(10, 0) datetime.time(7, 30) datetime.time(6, 0)] Wednesday_Open - object - unique: 60 - nulls: 33123 [datetime.time(11, 0) datetime.time(17, 0) datetime.time(7, 0) datetime.time(0, 0) datetime.time(16, 0) NaT datetime.time(12, 0) datetime.time(10, 0) datetime.time(7, 30) datetime.time(6, 0)] Thursday_Open - object - unique: 62 - nulls: 29340 [datetime.time(11, 0) datetime.time(17, 0) datetime.time(7, 0) datetime.time(0, 0) NaT datetime.time(12, 0) datetime.time(10, 0) datetime.time(7, 30) datetime.time(6, 0) datetime.time(15, 0)] Saturday_Open - object - unique: 59 - nulls: 33299 [datetime.time(11, 0) datetime.time(17, 0) datetime.time(7, 0) datetime.time(0, 0) datetime.time(16, 0) NaT datetime.time(12, 0) datetime.time(10, 0) datetime.time(7, 30) datetime.time(6, 0)] Sunday_Open - object - unique: 62 - nulls: 85770 [NaT datetime.time(11, 0) datetime.time(17, 0) datetime.time(7, 0) datetime.time(0, 0) datetime.time(4, 30) datetime.time(12, 0) datetime.time(10, 0) datetime.time(7, 30) datetime.time(6, 0)] Monday_Close - object - unique: 69 - nulls: 67102 [datetime.time(20, 0) datetime.time(22, 30) datetime.time(22, 0) datetime.time(15, 0) datetime.time(0, 0) datetime.time(23, 0) datetime.time(21, 0) datetime.time(2, 30) NaT datetime.time(21, 30)] Tuesday_Close - object - unique: 70 - nulls: 43461 [datetime.time(20, 0) datetime.time(22, 30) datetime.time(22, 0) datetime.time(15, 0) datetime.time(0, 0) datetime.time(23, 0) datetime.time(21, 0) datetime.time(2, 30) NaT datetime.time(1, 0)] Wednesday_Close - object - unique: 70 - nulls: 33123 [datetime.time(20, 0) datetime.time(22, 30) datetime.time(22, 0) datetime.time(15, 0) datetime.time(0, 0) datetime.time(23, 0) datetime.time(21, 0) datetime.time(2, 30) NaT datetime.time(1, 0)] Thursday_Close - object - unique: 77 - nulls: 29340 [datetime.time(20, 0) datetime.time(22, 30) datetime.time(22, 0) datetime.time(15, 0) datetime.time(0, 0) datetime.time(23, 0) datetime.time(21, 0) datetime.time(2, 30) NaT datetime.time(1, 0)] Saturday_Close - object - unique: 76 - nulls: 33299 [datetime.time(4, 0) datetime.time(22, 30) datetime.time(16, 0) datetime.time(0, 0) datetime.time(1, 0) datetime.time(22, 0) datetime.time(2, 30) NaT datetime.time(2, 0) datetime.time(21, 30)] Sunday_Close - object - unique: 71 - nulls: 85770 [NaT datetime.time(22, 30) datetime.time(22, 0) datetime.time(16, 0) datetime.time(0, 0) datetime.time(23, 0) datetime.time(21, 0) datetime.time(2, 30) datetime.time(21, 30) datetime.time(15, 0)] user_name - object - unique: 41141 - nulls: 0 ['Jexie' 'Alex' 'Mermaid' 'Jen' 'Kit' 'Y' 'Belinda' 'Jim' 'Marko' 'Tien'] yelping_since - object - unique: 4618 - nulls: 0 [datetime.date(2009, 6, 24) datetime.date(2015, 7, 27) datetime.date(2012, 1, 1) datetime.date(2013, 2, 27) datetime.date(2008, 11, 15) datetime.date(2011, 4, 16) datetime.date(2013, 6, 25) datetime.date(2011, 6, 7) datetime.date(2011, 6, 4) datetime.date(2016, 7, 5)]
Drop useless features
train_test_set.drop(columns=['date', 'name', 'address', 'yelping_since', 'user_name', 'cuisine'], inplace=True)
Fill missing values
train_test_set['OutdoorSeating'] = train_test_set['OutdoorSeating'].fillna('None')
train_test_set['BusinessAcceptsCreditCards'] = train_test_set['BusinessAcceptsCreditCards'].fillna('None')
train_test_set['RestaurantsDelivery'] = train_test_set['RestaurantsDelivery'].fillna('None')
train_test_set['RestaurantsReservations'] = train_test_set['RestaurantsReservations'].fillna('None')
train_test_set['WiFi'] = train_test_set['WiFi'].fillna('None')
train_test_set['Alcohol'] = train_test_set['Alcohol'].fillna('None')
train_test_set['Monday_Open'] = train_test_set["Monday_Open"].astype(str)
train_test_set['Monday_Open'] = train_test_set['Monday_Open'].fillna(train_test_set['Monday_Open'].mode())
train_test_set['Tuesday_Open'] = train_test_set["Tuesday_Open"].astype(str)
train_test_set['Tuesday_Open'] = train_test_set['Tuesday_Open'].fillna(train_test_set['Tuesday_Open'].mode())
train_test_set['Wednesday_Open'] = train_test_set["Wednesday_Open"].astype(str)
train_test_set['Wednesday_Open'] = train_test_set['Wednesday_Open'].fillna(train_test_set['Wednesday_Open'].mode())
train_test_set['Thursday_Open'] = train_test_set["Thursday_Open"].astype(str)
train_test_set['Thursday_Open'] = train_test_set['Thursday_Open'].fillna(train_test_set['Thursday_Open'].mode())
train_test_set['Friday_Open'] = train_test_set["Friday_Open"].astype(str)
train_test_set['Friday_Open'] = train_test_set['Friday_Open'].fillna(train_test_set['Friday_Open'].mode())
train_test_set['Saturday_Open'] = train_test_set["Saturday_Open"].astype(str)
train_test_set['Saturday_Open'] = train_test_set['Saturday_Open'].fillna(train_test_set['Saturday_Open'].mode())
train_test_set['Sunday_Open'] = train_test_set["Sunday_Open"].astype(str)
train_test_set['Sunday_Open'] = train_test_set['Sunday_Open'].fillna(train_test_set['Sunday_Open'].mode())
train_test_set['Monday_Close'] = train_test_set["Monday_Close"].astype(str)
train_test_set['Monday_Close'] = train_test_set['Monday_Close'].fillna(train_test_set['Monday_Close'].mode())
train_test_set['Tuesday_Close'] = train_test_set["Tuesday_Close"].astype(str)
train_test_set['Tuesday_Close'] = train_test_set['Tuesday_Close'].fillna(train_test_set['Tuesday_Close'].mode())
train_test_set['Wednesday_Close'] = train_test_set["Wednesday_Close"].astype(str)
train_test_set['Wednesday_Close'] = train_test_set['Wednesday_Close'].fillna(train_test_set['Wednesday_Close'].mode())
train_test_set['Thursday_Close'] = train_test_set["Thursday_Close"].astype(str)
train_test_set['Thursday_Close'] = train_test_set['Thursday_Close'].fillna(train_test_set['Thursday_Close'].mode())
train_test_set['Friday_Close'] = train_test_set["Friday_Close"].astype(str)
train_test_set['Friday_Close'] = train_test_set['Friday_Close'].fillna(train_test_set['Friday_Close'].mode())
train_test_set['Saturday_Close'] = train_test_set["Saturday_Close"].astype(str)
train_test_set['Saturday_Close'] = train_test_set['Saturday_Close'].fillna(train_test_set['Saturday_Close'].mode())
train_test_set['Sunday_Close'] = train_test_set["Sunday_Close"].astype(str)
train_test_set['Sunday_Close'] = train_test_set['Sunday_Close'].fillna(train_test_set['Sunday_Close'].mode())
for ind, dtype in train_test_types.iteritems():
if _np.issubdtype(dtype, _np.floating):
train_test_set[ind] = train_test_set[ind].fillna(train_test_set[ind].mean())
elif _np.issubdtype(dtype, _np.integer):
train_test_set[ind] = train_test_set[ind].fillna(round(train_test_set[ind].mean()))
# check if any feature still has null values
train_test_set.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 712379 entries, 0 to 153992 Data columns (total 93 columns): review_id 712379 non-null object user_id 712379 non-null object business_id 712379 non-null object stars_review 712379 non-null int64 useful_review 712379 non-null int64 funny_review 712379 non-null int64 cool_review 712379 non-null int64 bin_truth_score 712379 non-null int64 real_truth_score 712379 non-null float64 cuisine_av_hist 712379 non-null float64 cuisine_av_hist_bin 712379 non-null float64 cuisine_av_hist_real 712379 non-null float64 coll_score 712379 non-null float64 coll_score_bin 712379 non-null float64 coll_score_real 712379 non-null float64 likes 712379 non-null int32 postal_code 712379 non-null object latitude 712379 non-null float64 longitude 712379 non-null float64 review_count 712379 non-null int64 stars_restaurant 712379 non-null float64 OutdoorSeating 712379 non-null object BusinessAcceptsCreditCards 712379 non-null object RestaurantsDelivery 712379 non-null object RestaurantsReservations 712379 non-null object WiFi 712379 non-null object Alcohol 712379 non-null object categories 712379 non-null object city 712379 non-null object Monday_Open 712379 non-null object Tuesday_Open 712379 non-null object Wednesday_Open 712379 non-null object Thursday_Open 712379 non-null object Friday_Open 712379 non-null object Saturday_Open 712379 non-null object Sunday_Open 712379 non-null object Monday_Close 712379 non-null object Tuesday_Close 712379 non-null object Wednesday_Close 712379 non-null object Thursday_Close 712379 non-null object Friday_Close 712379 non-null object Saturday_Close 712379 non-null object Sunday_Close 712379 non-null object average_stars_review 712379 non-null float64 num_reviews_review 712379 non-null float64 average_stars_bin_review 712379 non-null float64 num_reviews_bin_review 712379 non-null float64 average_stars_real_review 712379 non-null float64 num_reviews_real_review 712379 non-null float64 compliment_count 712379 non-null float64 average_stars_user 712379 non-null float64 review 712379 non-null int64 years_of_elite 712379 non-null int64 fans 712379 non-null int64 useful_user 712379 non-null int64 cool_user 712379 non-null int64 funny_user 712379 non-null int64 friends 712379 non-null int64 num_reviews_user 712379 non-null float64 average_stars_bin_user 712379 non-null float64 num_reviews_bin_user 712379 non-null float64 average_stars_real_user 712379 non-null float64 num_reviews_real_user 712379 non-null float64 av_rat_chinese_cuisine 712379 non-null float64 av_rat_japanese_cuisine 712379 non-null float64 av_rat_mexican_cuisine 712379 non-null float64 av_rat_italian_cuisine 712379 non-null float64 av_rat_others_cuisine 712379 non-null float64 av_rat_american_cuisine 712379 non-null float64 av_rat_korean_cuisine 712379 non-null float64 av_rat_mediterranean_cuisine 712379 non-null float64 av_rat_thai_cuisine 712379 non-null float64 av_rat_asianfusion_cuisine 712379 non-null float64 av_rat_chinese_cuisine_bin 712379 non-null float64 av_rat_japanese_cuisine_bin 712379 non-null float64 av_rat_mexican_cuisine_bin 712379 non-null float64 av_rat_italian_cuisine_bin 712379 non-null float64 av_rat_others_cuisine_bin 712379 non-null float64 av_rat_american_cuisine_bin 712379 non-null float64 av_rat_korean_cuisine_bin 712379 non-null float64 av_rat_mediterranean_cuisine_bin 712379 non-null float64 av_rat_thai_cuisine_bin 712379 non-null float64 av_rat_asianfusion_cuisine_bin 712379 non-null float64 av_rat_chinese_cuisine_real 712379 non-null float64 av_rat_japanese_cuisine_real 712379 non-null float64 av_rat_mexican_cuisine_real 712379 non-null float64 av_rat_italian_cuisine_real 712379 non-null float64 av_rat_others_cuisine_real 712379 non-null float64 av_rat_american_cuisine_real 712379 non-null float64 av_rat_korean_cuisine_real 712379 non-null float64 av_rat_mediterranean_cuisine_real 712379 non-null float64 av_rat_thai_cuisine_real 712379 non-null float64 av_rat_asianfusion_cuisine_real 712379 non-null float64 dtypes: float64(53), int32(1), int64(13), object(26) memory usage: 508.2+ MB
Convert non-numerical features
train_test_set.shape
(712379, 93)
cat_cols = ['OutdoorSeating', 'BusinessAcceptsCreditCards', 'RestaurantsDelivery', 'RestaurantsReservations', 'WiFi',
'Alcohol', 'city', 'Monday_Open', 'Tuesday_Open', 'Wednesday_Open', 'Thursday_Open', 'Friday_Open',
'Saturday_Open', 'Sunday_Open', 'Monday_Close', 'Tuesday_Close', 'Wednesday_Close', 'Thursday_Close',
'Friday_Close', 'Saturday_Close', 'Sunday_Close']
train_test_set = _pd.get_dummies(train_test_set, columns=cat_cols, prefix=cat_cols)
train_test_set.shape
(712379, 1689)
oe = _OrdinalEncoder()
train_test_set['postal_code'] = oe.fit_transform(train_test_set['postal_code'].to_numpy().reshape(-1, 1))
train_test_set.shape
(712379, 1689)
categories = train_test_set['categories'].str.get_dummies(',')
f1 = lambda x: "categories_" + x
categories.rename(columns=f1, inplace=True)
train_test_set[categories.columns] = categories
train_test_set.drop(columns=['categories'], inplace=True)
train_test_set.shape
(712379, 2774)
The resulting dataset
train_test_set.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 712379 entries, 0 to 153992 Columns: 2774 entries, review_id to categories_Yoga dtypes: float64(54), int32(1), int64(1099), object(3), uint8(1617) memory usage: 7.2+ GB
train_set = train_test_set[:_train_len]
test_set = train_test_set[_train_len:]
train_set.head(10)
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Agincourt | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Aliquippa | city_Allison Park | city_Ambridge | city_Amherst | city_Ange-Gardien | city_Anjou | city_Anthem | city_Apache Junction | city_Arnold | city_Aspinwall | city_Auburn Township | city_Auburn Twp | city_Aurora | city_Avalon | city_Avon | city_Avon Lake | city_Avondale | city_Bainbridge | city_Bainbridge Township | city_Baldwin | city_Balzac | city_Bath | city_Bay Village | city_Beachwood | city_Beaconsfield | city_Beauharnois | city_Bedford | city_Bedford Heights | city_Beeton | city_Belle Vernon | city_Belleville | city_Bellevue | city_Bellvue | city_Belmont | city_Beloeil | city_Ben Avon | city_Berea | city_Bethel Park | city_Black Earth | city_Blainville | city_Blakeney | city_Blawnox | city_Blue Diamond | city_Bois-des-Filion | city_Boisbriand | city_Bolton | city_Boucherville | city_Boulder City | city_Braddock | city_Bradford | city_Bradford West 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| Monday_Open_08:30:00 | Monday_Open_09:00:00 | Monday_Open_09:15:00 | Monday_Open_09:30:00 | Monday_Open_09:45:00 | Monday_Open_10:00:00 | Monday_Open_10:15:00 | Monday_Open_10:30:00 | Monday_Open_10:45:00 | Monday_Open_11:00:00 | Monday_Open_11:30:00 | Monday_Open_11:45:00 | Monday_Open_12:00:00 | Monday_Open_12:15:00 | Monday_Open_12:30:00 | Monday_Open_12:45:00 | Monday_Open_13:00:00 | Monday_Open_13:15:00 | Monday_Open_13:30:00 | Monday_Open_14:00:00 | Monday_Open_14:30:00 | Monday_Open_15:00:00 | Monday_Open_15:30:00 | Monday_Open_16:00:00 | Monday_Open_16:15:00 | Monday_Open_16:30:00 | Monday_Open_16:45:00 | Monday_Open_17:00:00 | Monday_Open_17:30:00 | Monday_Open_18:00:00 | Monday_Open_18:30:00 | Monday_Open_19:00:00 | Monday_Open_20:00:00 | Monday_Open_20:30:00 | Monday_Open_21:00:00 | Monday_Open_22:00:00 | Monday_Open_22:30:00 | Monday_Open_23:00:00 | Monday_Open_NaT | Tuesday_Open_00:00:00 | Tuesday_Open_01:00:00 | Tuesday_Open_03:00:00 | Tuesday_Open_03:30:00 | Tuesday_Open_04:00:00 | Tuesday_Open_04:15:00 | Tuesday_Open_04:30:00 | Tuesday_Open_04:45:00 | Tuesday_Open_05:00:00 | Tuesday_Open_05:30:00 | Tuesday_Open_05:45:00 | Tuesday_Open_06:00:00 | Tuesday_Open_06:30:00 | Tuesday_Open_06:45:00 | Tuesday_Open_07:00:00 | Tuesday_Open_07:30:00 | Tuesday_Open_07:45:00 | Tuesday_Open_08:00:00 | Tuesday_Open_08:15:00 | Tuesday_Open_08:30:00 | Tuesday_Open_09:00:00 | Tuesday_Open_09:30:00 | Tuesday_Open_10:00:00 | Tuesday_Open_10:15:00 | Tuesday_Open_10:30:00 | Tuesday_Open_10:45:00 | Tuesday_Open_11:00:00 | Tuesday_Open_11:15:00 | Tuesday_Open_11:30:00 | Tuesday_Open_11:45:00 | Tuesday_Open_12:00:00 | Tuesday_Open_12:15:00 | Tuesday_Open_12:30:00 | Tuesday_Open_13:00:00 | Tuesday_Open_13:30:00 | Tuesday_Open_14:00:00 | Tuesday_Open_14:30:00 | Tuesday_Open_15:00:00 | Tuesday_Open_15:30:00 | Tuesday_Open_16:00:00 | Tuesday_Open_16:15:00 | Tuesday_Open_16:30:00 | Tuesday_Open_17:00:00 | Tuesday_Open_17:15:00 | Tuesday_Open_17:30:00 | Tuesday_Open_18:00:00 | Tuesday_Open_18:30:00 | Tuesday_Open_19:00:00 | Tuesday_Open_20:00:00 | Tuesday_Open_20:30:00 | Tuesday_Open_21:00:00 | Tuesday_Open_21:15:00 | Tuesday_Open_22:00:00 | Tuesday_Open_22:30:00 | Tuesday_Open_23:00:00 | Tuesday_Open_NaT | Wednesday_Open_00:00:00 | Wednesday_Open_00:15:00 | Wednesday_Open_01:00:00 | Wednesday_Open_01:30:00 | Wednesday_Open_03:00:00 | Wednesday_Open_03:15:00 | Wednesday_Open_03:30:00 | Wednesday_Open_04:00:00 | Wednesday_Open_04:15:00 | Wednesday_Open_04:30:00 | Wednesday_Open_04:45:00 | Wednesday_Open_05:00:00 | Wednesday_Open_05:30:00 | Wednesday_Open_05:45:00 | Wednesday_Open_06:00:00 | Wednesday_Open_06:30:00 | Wednesday_Open_06:45:00 | Wednesday_Open_07:00:00 | Wednesday_Open_07:30:00 | Wednesday_Open_07:45:00 | Wednesday_Open_08:00:00 | Wednesday_Open_08:15:00 | Wednesday_Open_08:30:00 | Wednesday_Open_09:00:00 | Wednesday_Open_09:15:00 | Wednesday_Open_09:30:00 | Wednesday_Open_10:00:00 | Wednesday_Open_10:15:00 | Wednesday_Open_10:30:00 | Wednesday_Open_10:45:00 | Wednesday_Open_11:00:00 | Wednesday_Open_11:15:00 | Wednesday_Open_11:30:00 | Wednesday_Open_11:45:00 | Wednesday_Open_12:00:00 | Wednesday_Open_12:15:00 | Wednesday_Open_12:30:00 | Wednesday_Open_13:00:00 | Wednesday_Open_13:30:00 | Wednesday_Open_14:00:00 | Wednesday_Open_14:30:00 | Wednesday_Open_15:00:00 | Wednesday_Open_15:30:00 | Wednesday_Open_16:00:00 | Wednesday_Open_16:15:00 | Wednesday_Open_16:30:00 | Wednesday_Open_17:00:00 | Wednesday_Open_17:15:00 | Wednesday_Open_17:30:00 | Wednesday_Open_18:00:00 | Wednesday_Open_18:30:00 | Wednesday_Open_19:00:00 | Wednesday_Open_19:30:00 | Wednesday_Open_20:00:00 | Wednesday_Open_20:30:00 | Wednesday_Open_21:00:00 | Wednesday_Open_22:00:00 | Wednesday_Open_22:30:00 | Wednesday_Open_23:00:00 | Wednesday_Open_NaT | Thursday_Open_00:00:00 | Thursday_Open_00:15:00 | Thursday_Open_01:00:00 | Thursday_Open_02:00:00 | Thursday_Open_03:00:00 | Thursday_Open_03:30:00 | Thursday_Open_04:00:00 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categories_Veterinarians | categories_Video Game Stores | categories_Videos & Video Game Rental | categories_Vietnamese | categories_Vitamins & Supplements | categories_Waffles | categories_Waxing | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wholesalers | categories_Wigs | categories_Wine Bars | categories_Wine Tasting Classes | categories_Wine Tasting Room | categories_Wine Tours | categories_Wineries | categories_Wraps | categories_Yoga |
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| 5 | rPNt-m2pdt-OR_NMQnHjCQ | tKIihU81IA3NjpsADuR-Tg | --6MefnULPED_I942VcFNA | 5 | 3 | 0 | 2 | 1 | 0.907921 | 4.400000 | 4.400000 | 4.395327 | 4.376569 | 4.405801 | 4.313458 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 4.111111 | 230 | 2 | 12 | 166 | 70 | 32 | 2326 | 9.0 | 4.111111 | 9.0 | 4.132161 | 8.602213 | 4.400000 | 3.79608 | 3.686094 | 3.777956 | 4.500000 | 3.684951 | 3.789846 | 1.000000 | 3.868171 | 5.000000 | 4.400000 | 3.763461 | 3.662669 | 3.749776 | 4.500000 | 3.66752 | 3.771654 | 1.000000 | 3.851784 | 5.000000 | 4.395327 | 3.789966 | 3.678871 | 3.770170 | 4.481197 | 3.676024 | 3.788204 | 1.000000 | 3.867280 | 5.000000 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 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| 6 | Kg582pH05mZO_E6WS8PrKA | XNOs3Wz1Q_zdRgm1Hy05fg | --6MefnULPED_I942VcFNA | 1 | 2 | 2 | 1 | -1 | 0.184327 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 3.703313 | 1 | 0 | 0 | 2 | 1 | 2 | 1462 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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train_set.shape
(558386, 2774)
train_set.to_pickle('../dataset/m2_n9/model_train_set_2.pickle')
test_set.head(10)
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Agincourt | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Aliquippa | city_Allison Park | city_Ambridge | city_Amherst | city_Ange-Gardien | city_Anjou | city_Anthem | city_Apache Junction | city_Arnold | city_Aspinwall | city_Auburn Township | city_Auburn Twp | city_Aurora | city_Avalon | city_Avon | city_Avon Lake | city_Avondale | city_Bainbridge | city_Bainbridge Township | city_Baldwin | city_Balzac | city_Bath | city_Bay Village | city_Beachwood | city_Beaconsfield | city_Beauharnois | city_Bedford | city_Bedford Heights | city_Beeton | city_Belle Vernon | city_Belleville | city_Bellevue | city_Bellvue | city_Belmont | city_Beloeil | city_Ben Avon | city_Berea | city_Bethel Park | city_Black Earth | city_Blainville | city_Blakeney | city_Blawnox | city_Blue Diamond | city_Bois-des-Filion | city_Boisbriand | city_Bolton | city_Boucherville | city_Boulder City | city_Braddock | city_Bradford | city_Bradford West Gwillimbury | city_Brampton | city_Bratenahl | city_Brecksville | city_Brentwood | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklin | city_Brookline | city_Brooklyn | city_Brookpark | city_Brossard | city_Brownsburg-Chatham | city_Brunswick | city_Brunswick Hills | city_Buckeye | city_Buena Vista | city_Burton | city_Caledon | city_Caledon East | city_Caledon Village | city_Calgary | city_Candiac | city_Canonsburd | city_Canonsburg | city_Carefree | city_Carnegie | city_Castle Shannon | city_Cave Creek | city_Cecil | city_Central | city_Central City | city_Central City Village | city_Chagrin Falls | city_Chambly | city_Champaign | city_Champlain | city_Chandler | city_Chardon | city_Chargrin Falls | city_Charlemagne | city_Charlotte | city_Chatauguay | city_Chateauguay | city_Chesterland | city_Chestermere | city_Cheswick | city_Châteauguay | city_Clairton | city_Clark | city_Clarkson | city_Cleveland | city_Cleveland Heights | city_Cleveland Hghts. | city_Clover | city_Columbia Station | city_Communauté-Urbaine-de-Montréal | city_Concord | city_Concord Mills | city_Concord Township | city_Copley | city_Coraopolis | city_Cornelius | city_Cote Saint-Luc | city_Coteau-du-Lac | city_Cottage Grove | city_Crafton | city_Cramerton | city_Creighton | city_Cross Plains | city_Cuddy | city_Cuyahoga Falls | city_Cuyahoga Fls | city_Cuyahoga Heights | city_Dallas | city_Dane | city_Davidson | city_De Forest | city_De Winton | city_DeForest | city_Deerfield | city_Deforest | city_Delson | city_Denver | city_Dollard-Des Ormeaux | city_Dollard-Des-Ormeaux | city_Dollard-des Ormeaux | city_Dollard-des-Ormeaux | city_Don Mills | city_Dorval | city_Downtown | city_Dravosburg | city_Duquesne | city_ETOBICOKE | city_East Ajax | city_East Cleveland | city_East Gwillimbury | city_East Liberty | city_East Mc Keesport | city_East McKeesport | city_East Pittsburgh | city_East York | city_Eastlake | city_Edgewood | city_El Mirage | city_Elizabeth | city_Elizabeth Township | city_Elrama | city_Elyria | city_Emsworth | city_Enterprise | city_Estérel | city_Etibicoke | city_Etna | city_Etobicoke | city_Etobiicoke | city_Euclid | city_Export | city_Fabreville | city_Fairlawn | city_Fairport Harbor | city_Fairview Park | city_Finleyville | city_Fisher | city_Fitchburg | city_Forest Hills | city_Fort McDowell | city_Fort Mcdowell | city_Fort Mill | city_Fountain Hills | city_GILBERT | city_GOODYEAR | city_Garfield Heights | city_Gastonia | city_Gates Mills | city_Georgetown | city_Gibsonia | city_Gifford | city_Gilbert | city_Glassport | city_Glen Williams | city_Glendale | city_Glendale Az | city_Glenshaw | city_Goodwood | city_Goodyear | city_Grafton | city_Grand River | city_Green Tree | city_Greenfield Park | city_Guadalupe | city_Halton Hills | city_Hampstead | city_Hampton Township | city_Harmarville | city_Harrisbug | city_Harrisburg | city_Harrison City | city_Harwick | city_Heidelberg | city_Hemmingford | city_Henderson | city_Hendersonville | city_Henryville | city_Herminie | city_Highland Heights | city_Higley | city_Hinckley | city_Holland Landing | city_Homer | city_Homestead | city_Hudson | city_Huntersville | city_Huntingdon | city_Hyland Heights | city_Iberville | city_Imperial | city_Independence | city_Indian Land | city_Indian Trail | city_Indian land | city_Indianola | city_Inglewood | city_Irwin | city_Jefferson Hills | city_Joliette | city_Kahnawake | city_Kannapolis | city_Kennedy Township | city_Kent | city_King | city_King City | city_Kirkland | city_Kirtland | city_Kleinburg | city_L'Assomption | city_L'ile-Perrot | city_L'Île-Bizard | city_L'Île-Perrot | city_LAS VEGAS | city_La Prairie | city_La Salle | city_LaGrange | city_LaSalle | city_Lachine | city_Lachute | city_Lagrange | city_Lake Park | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Las Vegas | city_LasVegas | city_Lasalle | city_Laval | city_Laval, Ste Dorothee | city_Laveen | city_Laveen Village | city_Lawrence | city_Lawrenceville | city_Leetsdale | city_Les Coteaux | city_Les Cèdres | city_Library | city_Lindale | city_Litchfield | city_Litchfield Park | city_Locust | city_Longueuil | city_Lorain | city_Lowell | city_Lower Burrell | city_Lower Lawrenceville | city_Lower burrell | city_Lowesville | city_Lyndhurst | city_MESA | city_Macedonia | city_Madison | city_Mahomet | city_Malton | city_Mantua | city_Maple | city_Maple Heights | city_Markham | city_Marshall | city_Mascouche | city_Matthews | city_Mayfield | city_Mayfield Heights | city_Mayfield Village | city_Mc Donald | city_Mc Farland | city_Mc Kees Rocks | city_Mc Murray | city_McAdenville | city_McCandless | city_McCandless Township | city_McDonald | city_McFarland | city_McKees Rocks | city_McKeesport | city_McMurray | city_Mcfarland | city_Mckees Rocks | city_Mckeesport | city_Mcknight | city_Mcmurray | city_Medina | city_Medina Township | city_Mentor | city_Mentor On The Lake | city_Mentor On the Lake | city_Mentor-on-the-Lake | city_Mercier | city_Mesa | city_Mesa AZ | city_Middleburg Heights | city_Middlefield | city_Middleton | city_Midland | city_Midnapore | city_Midway | city_Millvale | city_Milton | city_Mint Hill | city_Mint Hill | city_Mirabel | city_Missisauga | city_Mississauga | city_Mississuaga | city_Monona | city_Monongahela | city_Monroe | city_Monroeville | city_Mont-Royal | city_Mont-Saint-Grégoire | city_Mont-Saint-Hilaire | city_Montgomery | city_Monticello | city_Montreal | city_Montreal-Est | city_Montreal-Nord | city_Montreal-West | city_Montrose | city_Montréal | city_Montréal-Nord | city_Montréal-Ouest | city_Montréal-West | city_Mooers | city_Moon | city_Moon TWP | city_Moon Township | city_Mooresville | city_Moreland Hills | city_Morin-Heights | city_Moseley | city_Mount Albert | city_Mount Holly | city_Mount Horeb | city_Mount Lebanon | city_Mount Oliver | city_Mount Washington | city_Mt Holly | city_Mt Lebanon | city_Mt. Holly | city_Mt. Lebanon | city_Munhall | city_Munroe Falls | city_Murrysville | city_N Las Vegas | city_N Ridgeville | city_N. Las Vegas | city_NELLIS AFB | city_Napierville | city_Nellis AFB | city_Nellis Air Force Base | city_Neville Island | city_New Eagle | city_New Kensington | city_Newbury | city_Newmarket | city_Nobleton | city_North York | city_North Braddock | city_North Huntingdon | city_North Huntington | city_North Las Vegas | city_North Olmstead | city_North Olmsted | city_North Randall | city_North Ridgeville | city_North Royalton | city_North Strabane Township | city_North Toronto | city_North Versailles | city_North York | city_Northfield | city_Northfield Center Township | city_Northwest Calgary | city_Norton | city_Oak Ridges | city_Oakdale | city_Oakland | city_Oakmont | city_Oakville | city_Oakwood | city_Oakwood Village | city_Ogden | city_Olmsted Falls | city_Olmsted Township | city_Omaha | city_Orange | city_Orange Village | city_Oregon | city_Outremont | city_PHOENIX | city_Painesville | city_Painesville Township | city_Palgrave | city_Paoli | city_Paradise | city_Paradise Valley | city_Parma | city_Parma Heights | city_Paw Creek | city_Peninsula | city_Penn Hills | city_Penn Hills Township | city_Peoria | city_Pepper Pike | city_Peters Township | city_Phoenix | city_Phx | city_Pickering | city_Pierrefonds | city_Pincourt | city_Pineville | city_Pitcairn | city_Pittsburgh | city_Pleasant Hills | city_Plum | city_Plum Boro | city_Pointe-Aux-Trembles | city_Pointe-Calumet | city_Pointe-Claire | city_Port Credit | city_Port Vue | city_Presto | city_Queen Creek | city_Québec | city_Ranlo | city_Rantoul | city_Ravenna | city_Rawdon | city_Repentigny | city_Rexdale | city_Richfield | city_Richmond Height | city_Richmond Heights | city_Richmond Hil | city_Richmond Hill | city_Richmond Hts | city_Rigaud | city_Rillton | city_Rio Verde | city_River Drive Park | city_Robinson | city_Robinson Township | city_Robinson Twp. | city_Rock Hill | city_Rocky River | city_Rocky View | city_Rocky View County | city_Rocky View No. 44 | city_Rosemere | city_Rosemère | city_Ross | city_Ross Township | city_Rostraver | city_Rouses Point | city_Rural Ridge | city_Russellton | city_SCOTTSDALE | city_Sagamore Hills | city_Saint Joseph | city_Saint Laurent | city_Saint-Basile-Le-Grand | city_Saint-Basile-le-Grand | city_Saint-Bruno | city_Saint-Bruno-de-Montarville | city_Saint-Constant | city_Saint-Eustache | city_Saint-Henri | city_Saint-Hippolyte | city_Saint-Hubert | city_Saint-Hyacinthe | city_Saint-Jean-sur-Richelieu | city_Saint-Jerome | city_Saint-Jérôme | city_Saint-Lambert | city_Saint-Laurent | city_Saint-Lazare | city_Saint-Leonard | city_Saint-Léonard | city_Saint-Marc-sur-Richelieu | city_Saint-Sauveur | city_Saint-laurent | city_Sainte-Adele | city_Sainte-Adèle | city_Sainte-Anne-De-Bellevue | city_Sainte-Anne-de-Bellevue | city_Sainte-Catherine | city_Sainte-Genevieve | city_Sainte-Julie | city_Sainte-Marguerite-Esterel | city_Sainte-Marguerite-du-lac-Masson | city_Sainte-Marthe | city_Sainte-Thérèse | city_Sainte-Thérèse-de-Blainville | city_Saintt-Bruno-de-Montarville | city_Salaberry-De-Valleyfield | city_Salaberry-de-Valleyfield | city_Sauk City | city_Savoy | city_Scarborough | city_Scarobrough | city_Schomberg | city_Schottsdale | city_Scottdale | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Shaker Heights | city_Shaler Township | city_Sharpsburg | city_Sheffield | city_Sheffield Lake | city_Sheffield Village | city_Shorewood Hills | city_Solon | city_South Amherst | city_South Euclid | city_South Las Vegas | city_South Park | city_South Park Township | city_Spring Valley | city_Springdale | city_St Joseph | city_St Leonard | city_St-Benoît de Mirabel | city_St-Bruno-de-Montarville | city_St-Jerome | city_St-Leonard | city_St. Léonard | city_Stallings | city_Stanley | city_Ste-Rose | city_Stouffville | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sturgeon | city_Summerlin | city_Sun City | city_Sun City West | city_Sun Lakes | city_Sun Praiie | city_Sun Prairie | city_Sunrise Manor | city_Surprise | city_Swissvale | city_THORNHILL | city_TORONTO | city_Tallmadge | city_Tarentum | city_Tega Cay | city_Tempe | city_Terrebonne | city_Thorncliffe Park | city_Thornhil | city_Thornhill | city_Tolleson | city_Tolono | city_Tornto | city_Toronto | city_Toronto Scarborough | city_Tottenham | city_Trafford | city_Tremont | city_Troy Township | city_Turtle Creek | city_Tuscola | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Upper St Clair | city_Urbana | city_Uxbridge | city_Val-Morin | city_Valley City | city_Valley View | city_Vaudreuil-Dorion | city_Vaughan | city_Venetia | city_Venise-en-Québec | city_Verdun | city_Verona | city_Ville Mont-Royal | city_Vimont | city_Waddell | city_Wadsworth | city_Walton Hills | city_Warrensville Heights | city_Warrensville Hts. | city_Waterloo | city_Waunakee | city_Waxhaw | city_Weddington | city_Wesley Chapel | city_West Elizabeth | city_West Homestead | city_West Mifflin | city_West View | city_Westlake | city_Westmount | city_Wexford | city_Whiitby | city_Whitby | city_Whitchurch-Stouffville | city_White Oak | city_Wickliffe | city_Wilkins Township | city_Wilkinsburg | city_Willoughby | city_Willoughby Hills | city_Willowdale | city_Willowick | city_Wilmerding | city_Windsor | city_Woodbridge | city_Woodmere | city_Woodmere Village | city_York | city_Youngtown | city_cave creek | city_charlotte | city_kirtland | city_lyndhurst | city_solon | city_springdale | city_verdun | city_Île-des-Soeurs | Monday_Open_00:00:00 | Monday_Open_01:00:00 | Monday_Open_02:00:00 | Monday_Open_03:00:00 | Monday_Open_03:30:00 | Monday_Open_04:00:00 | Monday_Open_04:15:00 | Monday_Open_04:30:00 | Monday_Open_04:45:00 | Monday_Open_05:00:00 | Monday_Open_05:30:00 | Monday_Open_05:45:00 | Monday_Open_06:00:00 | Monday_Open_06:30:00 | Monday_Open_06:45:00 | Monday_Open_07:00:00 | Monday_Open_07:30:00 | Monday_Open_07:45:00 | Monday_Open_08:00:00 | Monday_Open_08:15:00 | Monday_Open_08:30:00 | Monday_Open_09:00:00 | Monday_Open_09:15:00 | Monday_Open_09:30:00 | Monday_Open_09:45:00 | Monday_Open_10:00:00 | Monday_Open_10:15:00 | Monday_Open_10:30:00 | Monday_Open_10:45:00 | Monday_Open_11:00:00 | Monday_Open_11:30:00 | Monday_Open_11:45:00 | Monday_Open_12:00:00 | Monday_Open_12:15:00 | Monday_Open_12:30:00 | Monday_Open_12:45:00 | Monday_Open_13:00:00 | Monday_Open_13:15:00 | Monday_Open_13:30:00 | Monday_Open_14:00:00 | Monday_Open_14:30:00 | Monday_Open_15:00:00 | Monday_Open_15:30:00 | Monday_Open_16:00:00 | Monday_Open_16:15:00 | Monday_Open_16:30:00 | Monday_Open_16:45:00 | Monday_Open_17:00:00 | Monday_Open_17:30:00 | Monday_Open_18:00:00 | Monday_Open_18:30:00 | Monday_Open_19:00:00 | Monday_Open_20:00:00 | Monday_Open_20:30:00 | Monday_Open_21:00:00 | Monday_Open_22:00:00 | Monday_Open_22:30:00 | Monday_Open_23:00:00 | Monday_Open_NaT | Tuesday_Open_00:00:00 | Tuesday_Open_01:00:00 | Tuesday_Open_03:00:00 | Tuesday_Open_03:30:00 | Tuesday_Open_04:00:00 | Tuesday_Open_04:15:00 | Tuesday_Open_04:30:00 | Tuesday_Open_04:45:00 | Tuesday_Open_05:00:00 | Tuesday_Open_05:30:00 | Tuesday_Open_05:45:00 | Tuesday_Open_06:00:00 | Tuesday_Open_06:30:00 | Tuesday_Open_06:45:00 | Tuesday_Open_07:00:00 | Tuesday_Open_07:30:00 | Tuesday_Open_07:45:00 | Tuesday_Open_08:00:00 | Tuesday_Open_08:15:00 | Tuesday_Open_08:30:00 | Tuesday_Open_09:00:00 | Tuesday_Open_09:30:00 | Tuesday_Open_10:00:00 | Tuesday_Open_10:15:00 | Tuesday_Open_10:30:00 | Tuesday_Open_10:45:00 | Tuesday_Open_11:00:00 | Tuesday_Open_11:15:00 | Tuesday_Open_11:30:00 | Tuesday_Open_11:45:00 | Tuesday_Open_12:00:00 | Tuesday_Open_12:15:00 | Tuesday_Open_12:30:00 | Tuesday_Open_13:00:00 | Tuesday_Open_13:30:00 | Tuesday_Open_14:00:00 | Tuesday_Open_14:30:00 | Tuesday_Open_15:00:00 | Tuesday_Open_15:30:00 | Tuesday_Open_16:00:00 | Tuesday_Open_16:15:00 | Tuesday_Open_16:30:00 | Tuesday_Open_17:00:00 | Tuesday_Open_17:15:00 | Tuesday_Open_17:30:00 | Tuesday_Open_18:00:00 | Tuesday_Open_18:30:00 | Tuesday_Open_19:00:00 | Tuesday_Open_20:00:00 | Tuesday_Open_20:30:00 | Tuesday_Open_21:00:00 | Tuesday_Open_21:15:00 | Tuesday_Open_22:00:00 | Tuesday_Open_22:30:00 | Tuesday_Open_23:00:00 | Tuesday_Open_NaT | Wednesday_Open_00:00:00 | Wednesday_Open_00:15:00 | Wednesday_Open_01:00:00 | Wednesday_Open_01:30:00 | Wednesday_Open_03:00:00 | Wednesday_Open_03:15:00 | Wednesday_Open_03:30:00 | Wednesday_Open_04:00:00 | Wednesday_Open_04:15:00 | Wednesday_Open_04:30:00 | Wednesday_Open_04:45:00 | Wednesday_Open_05:00:00 | Wednesday_Open_05:30:00 | Wednesday_Open_05:45:00 | Wednesday_Open_06:00:00 | Wednesday_Open_06:30:00 | Wednesday_Open_06:45:00 | Wednesday_Open_07:00:00 | Wednesday_Open_07:30:00 | Wednesday_Open_07:45:00 | Wednesday_Open_08:00:00 | Wednesday_Open_08:15:00 | Wednesday_Open_08:30:00 | Wednesday_Open_09:00:00 | Wednesday_Open_09:15:00 | Wednesday_Open_09:30:00 | Wednesday_Open_10:00:00 | Wednesday_Open_10:15:00 | Wednesday_Open_10:30:00 | Wednesday_Open_10:45:00 | Wednesday_Open_11:00:00 | Wednesday_Open_11:15:00 | Wednesday_Open_11:30:00 | Wednesday_Open_11:45:00 | Wednesday_Open_12:00:00 | Wednesday_Open_12:15:00 | Wednesday_Open_12:30:00 | Wednesday_Open_13:00:00 | Wednesday_Open_13:30:00 | Wednesday_Open_14:00:00 | Wednesday_Open_14:30:00 | Wednesday_Open_15:00:00 | Wednesday_Open_15:30:00 | Wednesday_Open_16:00:00 | Wednesday_Open_16:15:00 | Wednesday_Open_16:30:00 | Wednesday_Open_17:00:00 | Wednesday_Open_17:15:00 | Wednesday_Open_17:30:00 | Wednesday_Open_18:00:00 | Wednesday_Open_18:30:00 | Wednesday_Open_19:00:00 | Wednesday_Open_19:30:00 | Wednesday_Open_20:00:00 | Wednesday_Open_20:30:00 | Wednesday_Open_21:00:00 | Wednesday_Open_22:00:00 | Wednesday_Open_22:30:00 | Wednesday_Open_23:00:00 | Wednesday_Open_NaT | Thursday_Open_00:00:00 | Thursday_Open_00:15:00 | Thursday_Open_01:00:00 | Thursday_Open_02:00:00 | Thursday_Open_03:00:00 | Thursday_Open_03:30:00 | Thursday_Open_04:00:00 | Thursday_Open_04:15:00 | Thursday_Open_04:30:00 | Thursday_Open_04:45:00 | Thursday_Open_05:00:00 | Thursday_Open_05:30:00 | Thursday_Open_05:45:00 | Thursday_Open_06:00:00 | Thursday_Open_06:30:00 | Thursday_Open_06:45:00 | Thursday_Open_07:00:00 | Thursday_Open_07:30:00 | Thursday_Open_07:45:00 | Thursday_Open_08:00:00 | Thursday_Open_08:15:00 | Thursday_Open_08:30:00 | Thursday_Open_09:00:00 | Thursday_Open_09:15:00 | Thursday_Open_09:30:00 | Thursday_Open_10:00:00 | Thursday_Open_10:15:00 | Thursday_Open_10:30:00 | Thursday_Open_10:45:00 | Thursday_Open_11:00:00 | Thursday_Open_11:15:00 | Thursday_Open_11:30:00 | Thursday_Open_11:45:00 | Thursday_Open_12:00:00 | Thursday_Open_12:15:00 | Thursday_Open_12:30:00 | Thursday_Open_13:00:00 | Thursday_Open_13:30:00 | Thursday_Open_14:00:00 | Thursday_Open_14:30:00 | Thursday_Open_15:00:00 | Thursday_Open_15:30:00 | Thursday_Open_16:00:00 | Thursday_Open_16:15:00 | Thursday_Open_16:30:00 | Thursday_Open_17:00:00 | Thursday_Open_17:15:00 | Thursday_Open_17:30:00 | Thursday_Open_17:45:00 | Thursday_Open_18:00:00 | Thursday_Open_18:30:00 | Thursday_Open_19:00:00 | Thursday_Open_19:30:00 | Thursday_Open_20:00:00 | Thursday_Open_20:30:00 | Thursday_Open_21:00:00 | Thursday_Open_21:30:00 | Thursday_Open_22:00:00 | Thursday_Open_22:30:00 | Thursday_Open_23:00:00 | Thursday_Open_23:30:00 | Thursday_Open_NaT | Friday_Open_00:00:00 | Friday_Open_01:00:00 | Friday_Open_03:00:00 | Friday_Open_03:30:00 | Friday_Open_04:00:00 | Friday_Open_04:15:00 | Friday_Open_04:30:00 | Friday_Open_04:45:00 | Friday_Open_05:00:00 | Friday_Open_05:30:00 | Friday_Open_05:45:00 | Friday_Open_06:00:00 | Friday_Open_06:30:00 | Friday_Open_06:45:00 | Friday_Open_07:00:00 | Friday_Open_07:30:00 | Friday_Open_07:45:00 | Friday_Open_08:00:00 | Friday_Open_08:15:00 | Friday_Open_08:30:00 | Friday_Open_09:00:00 | Friday_Open_09:15:00 | Friday_Open_09:30:00 | Friday_Open_10:00:00 | Friday_Open_10:15:00 | Friday_Open_10:30:00 | Friday_Open_10:45:00 | Friday_Open_11:00:00 | Friday_Open_11:15:00 | Friday_Open_11:30:00 | Friday_Open_11:45:00 | Friday_Open_12:00:00 | Friday_Open_12:15:00 | Friday_Open_12:30:00 | Friday_Open_13:00:00 | Friday_Open_13:30:00 | Friday_Open_14:00:00 | Friday_Open_14:30:00 | Friday_Open_15:00:00 | Friday_Open_15:30:00 | Friday_Open_16:00:00 | Friday_Open_16:15:00 | Friday_Open_16:30:00 | Friday_Open_17:00:00 | Friday_Open_17:15:00 | Friday_Open_17:30:00 | Friday_Open_18:00:00 | Friday_Open_18:30:00 | Friday_Open_19:00:00 | Friday_Open_19:30:00 | Friday_Open_20:00:00 | Friday_Open_20:30:00 | Friday_Open_21:00:00 | Friday_Open_22:00:00 | Friday_Open_22:30:00 | Friday_Open_23:00:00 | Friday_Open_23:30:00 | Friday_Open_NaT | Saturday_Open_00:00:00 | Saturday_Open_01:00:00 | Saturday_Open_02:30:00 | Saturday_Open_03:00:00 | Saturday_Open_03:30:00 | Saturday_Open_04:00:00 | Saturday_Open_04:30:00 | Saturday_Open_04:45:00 | Saturday_Open_05:00:00 | Saturday_Open_05:30:00 | Saturday_Open_05:45:00 | Saturday_Open_06:00:00 | Saturday_Open_06:30:00 | Saturday_Open_06:45:00 | Saturday_Open_07:00:00 | Saturday_Open_07:30:00 | Saturday_Open_08:00:00 | Saturday_Open_08:15:00 | Saturday_Open_08:30:00 | Saturday_Open_09:00:00 | Saturday_Open_09:30:00 | Saturday_Open_10:00:00 | Saturday_Open_10:15:00 | Saturday_Open_10:30:00 | Saturday_Open_10:45:00 | Saturday_Open_11:00:00 | Saturday_Open_11:15:00 | Saturday_Open_11:30:00 | Saturday_Open_11:45:00 | Saturday_Open_12:00:00 | Saturday_Open_12:15:00 | Saturday_Open_12:30:00 | Saturday_Open_13:00:00 | Saturday_Open_13:30:00 | Saturday_Open_14:00:00 | Saturday_Open_14:30:00 | Saturday_Open_15:00:00 | Saturday_Open_15:30:00 | Saturday_Open_16:00:00 | Saturday_Open_16:15:00 | Saturday_Open_16:30:00 | Saturday_Open_17:00:00 | Saturday_Open_17:30:00 | Saturday_Open_18:00:00 | Saturday_Open_18:15:00 | Saturday_Open_18:30:00 | Saturday_Open_19:00:00 | Saturday_Open_19:30:00 | Saturday_Open_19:45:00 | Saturday_Open_20:00:00 | Saturday_Open_20:30:00 | Saturday_Open_21:00:00 | Saturday_Open_21:15:00 | Saturday_Open_21:30:00 | Saturday_Open_22:00:00 | Saturday_Open_22:30:00 | Saturday_Open_23:00:00 | Saturday_Open_23:30:00 | Saturday_Open_NaT | Sunday_Open_00:00:00 | Sunday_Open_00:30:00 | Sunday_Open_01:00:00 | Sunday_Open_02:00:00 | Sunday_Open_02:30:00 | Sunday_Open_03:00:00 | Sunday_Open_03:30:00 | Sunday_Open_04:00:00 | Sunday_Open_04:30:00 | Sunday_Open_04:45:00 | Sunday_Open_05:00:00 | Sunday_Open_05:30:00 | Sunday_Open_05:45:00 | Sunday_Open_06:00:00 | Sunday_Open_06:30:00 | Sunday_Open_06:45:00 | Sunday_Open_07:00:00 | Sunday_Open_07:30:00 | Sunday_Open_07:45:00 | Sunday_Open_08:00:00 | Sunday_Open_08:15:00 | Sunday_Open_08:30:00 | Sunday_Open_09:00:00 | Sunday_Open_09:30:00 | Sunday_Open_09:45:00 | Sunday_Open_10:00:00 | Sunday_Open_10:15:00 | Sunday_Open_10:30:00 | Sunday_Open_10:45:00 | Sunday_Open_11:00:00 | Sunday_Open_11:15:00 | Sunday_Open_11:30:00 | Sunday_Open_11:45:00 | Sunday_Open_12:00:00 | Sunday_Open_12:15:00 | Sunday_Open_12:30:00 | Sunday_Open_13:00:00 | Sunday_Open_13:15:00 | Sunday_Open_13:30:00 | Sunday_Open_14:00:00 | Sunday_Open_14:30:00 | Sunday_Open_15:00:00 | Sunday_Open_15:30:00 | Sunday_Open_16:00:00 | Sunday_Open_16:15:00 | Sunday_Open_16:30:00 | Sunday_Open_17:00:00 | Sunday_Open_17:15:00 | Sunday_Open_17:30:00 | Sunday_Open_18:00:00 | Sunday_Open_18:30:00 | Sunday_Open_19:00:00 | Sunday_Open_19:30:00 | Sunday_Open_20:00:00 | Sunday_Open_20:30:00 | Sunday_Open_21:00:00 | Sunday_Open_21:30:00 | Sunday_Open_22:00:00 | Sunday_Open_22:30:00 | Sunday_Open_23:00:00 | Sunday_Open_23:30:00 | Sunday_Open_NaT | Monday_Close_00:00:00 | Monday_Close_00:15:00 | Monday_Close_00:30:00 | Monday_Close_01:00:00 | Monday_Close_01:10:00 | Monday_Close_01:15:00 | Monday_Close_01:30:00 | Monday_Close_01:45:00 | Monday_Close_02:00:00 | Monday_Close_02:30:00 | Monday_Close_02:45:00 | Monday_Close_03:00:00 | Monday_Close_03:30:00 | Monday_Close_03:59:00 | Monday_Close_04:00:00 | Monday_Close_04:30:00 | Monday_Close_04:45:00 | Monday_Close_05:00:00 | Monday_Close_05:30:00 | Monday_Close_06:00:00 | Monday_Close_06:30:00 | Monday_Close_07:00:00 | Monday_Close_08:00:00 | Monday_Close_08:30:00 | Monday_Close_09:00:00 | Monday_Close_09:30:00 | Monday_Close_10:00:00 | Monday_Close_10:15:00 | Monday_Close_11:00:00 | Monday_Close_11:15:00 | Monday_Close_11:30:00 | Monday_Close_12:00:00 | Monday_Close_12:30:00 | Monday_Close_13:00:00 | Monday_Close_13:30:00 | Monday_Close_14:00:00 | Monday_Close_14:30:00 | Monday_Close_14:45:00 | Monday_Close_15:00:00 | Monday_Close_15:30:00 | Monday_Close_16:00:00 | Monday_Close_16:30:00 | Monday_Close_16:45:00 | Monday_Close_17:00:00 | Monday_Close_17:15:00 | Monday_Close_17:30:00 | Monday_Close_18:00:00 | Monday_Close_18:30:00 | Monday_Close_19:00:00 | Monday_Close_19:30:00 | Monday_Close_19:45:00 | Monday_Close_20:00:00 | Monday_Close_20:30:00 | Monday_Close_20:35:00 | Monday_Close_20:45:00 | Monday_Close_21:00:00 | Monday_Close_21:15:00 | Monday_Close_21:30:00 | Monday_Close_21:45:00 | Monday_Close_22:00:00 | Monday_Close_22:15:00 | Monday_Close_22:30:00 | Monday_Close_22:45:00 | Monday_Close_23:00:00 | Monday_Close_23:15:00 | Monday_Close_23:30:00 | Monday_Close_23:45:00 | Monday_Close_23:59:00 | Monday_Close_NaT | Tuesday_Close_00:00:00 | Tuesday_Close_00:15:00 | Tuesday_Close_00:30:00 | Tuesday_Close_00:45:00 | Tuesday_Close_01:00:00 | Tuesday_Close_01:10:00 | Tuesday_Close_01:15:00 | Tuesday_Close_01:30:00 | Tuesday_Close_01:45:00 | Tuesday_Close_02:00:00 | Tuesday_Close_02:30:00 | Tuesday_Close_03:00:00 | Tuesday_Close_03:30:00 | Tuesday_Close_03:59:00 | Tuesday_Close_04:00:00 | Tuesday_Close_04:30:00 | Tuesday_Close_04:45:00 | Tuesday_Close_05:00:00 | Tuesday_Close_05:30:00 | Tuesday_Close_06:00:00 | Tuesday_Close_07:00:00 | Tuesday_Close_08:00:00 | Tuesday_Close_08:30:00 | Tuesday_Close_09:00:00 | Tuesday_Close_09:30:00 | Tuesday_Close_10:00:00 | Tuesday_Close_10:15:00 | Tuesday_Close_10:30:00 | Tuesday_Close_11:00:00 | Tuesday_Close_11:30:00 | Tuesday_Close_12:00:00 | Tuesday_Close_12:30:00 | Tuesday_Close_12:45:00 | Tuesday_Close_13:00:00 | Tuesday_Close_13:30:00 | Tuesday_Close_14:00:00 | Tuesday_Close_14:30:00 | Tuesday_Close_14:45:00 | Tuesday_Close_15:00:00 | Tuesday_Close_15:30:00 | Tuesday_Close_15:45:00 | Tuesday_Close_16:00:00 | Tuesday_Close_16:30:00 | Tuesday_Close_16:45:00 | Tuesday_Close_17:00:00 | Tuesday_Close_17:15:00 | Tuesday_Close_17:30:00 | Tuesday_Close_18:00:00 | Tuesday_Close_18:30:00 | Tuesday_Close_19:00:00 | Tuesday_Close_19:30:00 | Tuesday_Close_19:45:00 | Tuesday_Close_20:00:00 | Tuesday_Close_20:15:00 | Tuesday_Close_20:30:00 | Tuesday_Close_20:35:00 | Tuesday_Close_20:45:00 | Tuesday_Close_21:00:00 | Tuesday_Close_21:15:00 | Tuesday_Close_21:30:00 | Tuesday_Close_21:45:00 | Tuesday_Close_22:00:00 | Tuesday_Close_22:15:00 | Tuesday_Close_22:30:00 | Tuesday_Close_22:45:00 | Tuesday_Close_23:00:00 | Tuesday_Close_23:30:00 | Tuesday_Close_23:45:00 | Tuesday_Close_23:59:00 | Tuesday_Close_NaT | Wednesday_Close_00:00:00 | Wednesday_Close_00:15:00 | Wednesday_Close_00:30:00 | Wednesday_Close_00:45:00 | Wednesday_Close_01:00:00 | Wednesday_Close_01:10:00 | Wednesday_Close_01:15:00 | Wednesday_Close_01:30:00 | Wednesday_Close_01:45:00 | Wednesday_Close_02:00:00 | Wednesday_Close_02:30:00 | Wednesday_Close_02:45:00 | Wednesday_Close_03:00:00 | Wednesday_Close_03:30:00 | Wednesday_Close_03:59:00 | Wednesday_Close_04:00:00 | Wednesday_Close_04:30:00 | Wednesday_Close_04:45:00 | Wednesday_Close_05:00:00 | Wednesday_Close_05:30:00 | Wednesday_Close_06:00:00 | Wednesday_Close_07:00:00 | Wednesday_Close_07:45:00 | Wednesday_Close_08:00:00 | Wednesday_Close_08:30:00 | Wednesday_Close_09:00:00 | Wednesday_Close_10:00:00 | Wednesday_Close_10:30:00 | Wednesday_Close_11:00:00 | Wednesday_Close_11:15:00 | Wednesday_Close_11:30:00 | Wednesday_Close_12:00:00 | Wednesday_Close_12:30:00 | Wednesday_Close_12:45:00 | Wednesday_Close_13:00:00 | Wednesday_Close_13:30:00 | Wednesday_Close_14:00:00 | Wednesday_Close_14:30:00 | Wednesday_Close_14:45:00 | Wednesday_Close_15:00:00 | Wednesday_Close_15:30:00 | Wednesday_Close_16:00:00 | Wednesday_Close_16:30:00 | Wednesday_Close_17:00:00 | Wednesday_Close_17:30:00 | Wednesday_Close_18:00:00 | Wednesday_Close_18:30:00 | Wednesday_Close_18:45:00 | Wednesday_Close_19:00:00 | Wednesday_Close_19:30:00 | Wednesday_Close_19:45:00 | Wednesday_Close_20:00:00 | Wednesday_Close_20:15:00 | Wednesday_Close_20:30:00 | Wednesday_Close_20:35:00 | Wednesday_Close_20:45:00 | Wednesday_Close_21:00:00 | Wednesday_Close_21:15:00 | Wednesday_Close_21:30:00 | Wednesday_Close_21:45:00 | Wednesday_Close_22:00:00 | Wednesday_Close_22:15:00 | Wednesday_Close_22:30:00 | Wednesday_Close_22:45:00 | Wednesday_Close_23:00:00 | Wednesday_Close_23:15:00 | Wednesday_Close_23:30:00 | Wednesday_Close_23:45:00 | Wednesday_Close_23:59:00 | Wednesday_Close_NaT | Thursday_Close_00:00:00 | Thursday_Close_00:15:00 | Thursday_Close_00:30:00 | Thursday_Close_00:45:00 | Thursday_Close_01:00:00 | Thursday_Close_01:10:00 | Thursday_Close_01:15:00 | Thursday_Close_01:30:00 | Thursday_Close_01:45:00 | Thursday_Close_02:00:00 | Thursday_Close_02:30:00 | Thursday_Close_02:45:00 | Thursday_Close_03:00:00 | Thursday_Close_03:30:00 | Thursday_Close_03:59:00 | Thursday_Close_04:00:00 | Thursday_Close_04:15:00 | Thursday_Close_04:30:00 | Thursday_Close_04:45:00 | Thursday_Close_05:00:00 | Thursday_Close_05:30:00 | Thursday_Close_06:00:00 | Thursday_Close_07:00:00 | Thursday_Close_07:45:00 | Thursday_Close_08:00:00 | Thursday_Close_08:30:00 | Thursday_Close_09:00:00 | Thursday_Close_09:30:00 | Thursday_Close_10:00:00 | Thursday_Close_10:30:00 | Thursday_Close_11:00:00 | Thursday_Close_11:15:00 | Thursday_Close_11:30:00 | Thursday_Close_11:45:00 | Thursday_Close_12:00:00 | Thursday_Close_12:30:00 | Thursday_Close_12:45:00 | Thursday_Close_13:00:00 | Thursday_Close_13:30:00 | Thursday_Close_14:00:00 | Thursday_Close_14:30:00 | Thursday_Close_14:45:00 | Thursday_Close_15:00:00 | Thursday_Close_15:15:00 | Thursday_Close_15:30:00 | Thursday_Close_16:00:00 | Thursday_Close_16:30:00 | Thursday_Close_17:00:00 | Thursday_Close_17:15:00 | Thursday_Close_17:30:00 | Thursday_Close_17:45:00 | Thursday_Close_18:00:00 | Thursday_Close_18:15:00 | Thursday_Close_18:30:00 | Thursday_Close_18:45:00 | Thursday_Close_19:00:00 | Thursday_Close_19:30:00 | Thursday_Close_19:45:00 | Thursday_Close_20:00:00 | Thursday_Close_20:15:00 | Thursday_Close_20:30:00 | Thursday_Close_20:45:00 | Thursday_Close_21:00:00 | Thursday_Close_21:15:00 | Thursday_Close_21:30:00 | Thursday_Close_21:35:00 | Thursday_Close_21:45:00 | Thursday_Close_22:00:00 | Thursday_Close_22:15:00 | Thursday_Close_22:30:00 | Thursday_Close_22:45:00 | Thursday_Close_23:00:00 | Thursday_Close_23:15:00 | Thursday_Close_23:30:00 | Thursday_Close_23:45:00 | Thursday_Close_23:59:00 | Thursday_Close_NaT | Friday_Close_00:00:00 | 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categories_Naturopathic/Holistic | categories_New Mexican Cuisine | categories_Nightlife | categories_Noodles | categories_Nutritionists | categories_Office Equipment | categories_Organic Stores | categories_Outlet Stores | categories_Pakistani | categories_Pan Asian | categories_Parks | categories_Party & Event Planning | categories_Pasta Shops | categories_Patisserie/Cake Shop | categories_Performing Arts | categories_Persian/Iranian | categories_Peruvian | categories_Pet Services | categories_Pets | categories_Pharmacy | categories_Photography Stores & Services | categories_Physical Therapy | categories_Piano Bars | categories_Pizza | categories_Poke | categories_Polish | categories_Pool Halls | categories_Pop-Up Restaurants | categories_Portuguese | categories_Poutineries | categories_Pretzels | categories_Professional Services | categories_Public Markets | categories_Public Services & Government | categories_Pubs | categories_Puerto Rican | categories_RV Parks | categories_Race Tracks | categories_Ramen | categories_Real Estate | categories_Real Estate Agents | categories_Recreation Centers | categories_Reflexology | categories_Resorts | categories_Restaurants | categories_Reunion | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Scottish | categories_Seafood | categories_Seafood Markets | categories_Service Stations | categories_Shanghainese | categories_Shared Office Spaces | categories_Shaved Ice | categories_Shaved Snow | categories_Shopping | categories_Shopping Centers | categories_Singaporean | categories_Skating Rinks | categories_Ski Resorts | categories_Skin Care | categories_Slovakian | categories_Smokehouse | categories_Soba | categories_Soccer | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Speakeasies | categories_Specialty Food | categories_Specialty Schools | categories_Sporting Goods | categories_Sports Bars | categories_Sports Clubs | categories_Sports Wear | categories_Spray Tanning | categories_Sri Lankan | categories_Stadiums & Arenas | categories_Steakhouses | categories_Street Vendors | categories_Strip Clubs | categories_Sugar Shacks | categories_Supper Clubs | categories_Sushi Bars | categories_Swimming Pools | categories_Swiss Food | categories_Syrian | categories_Szechuan | categories_Tacos | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Teppanyaki | categories_Tex-Mex | categories_Thai | categories_Themed Cafes | categories_Tiki Bars | categories_Tobacco Shops | categories_Tours | categories_Towing | categories_Trainers | categories_Transmission Repair | categories_Turkish | categories_Tuscan | categories_Ukrainian | categories_Used Bookstore | categories_Vegan | categories_Vegetarian | categories_Vehicle Wraps | categories_Venezuelan | categories_Venues & Event Spaces | categories_Veterinarians | categories_Video Game Stores | categories_Videos & Video Game Rental | categories_Vietnamese | categories_Vitamins & Supplements | categories_Waffles | categories_Waxing | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wholesalers | categories_Wigs | categories_Wine Bars | categories_Wine Tasting Classes | categories_Wine Tasting Room | categories_Wine Tours | categories_Wineries | categories_Wraps | categories_Yoga |
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| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
test_set.shape
(153993, 2774)
test_set.to_pickle('../dataset/m2_n9/model_test_set_2.pickle')
_del_all()
We summarize what kind of data we have at the moment, in order to decide what to do with each feature.
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set.pickle')
train_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | user_name | average_stars_user | yelping_since | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-01-11 19:55:31 | 1 | 0.622302 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Jexie | 3.846154 | 2009-06-24 | 18 | 0 | 1 | 8 | 4 | 4 | 4 | 13.0 | 4.222222 | 9.0 | 3.798692 | 11.168551 | NaN | 5.0 | NaN | NaN | 3.857143 | NaN | 3.0 | 5.0 | NaN | 2.00 | NaN | 5.0 | NaN | NaN | 5.000000 | NaN | 3.00 | 5.0 | NaN | 2.000000 | NaN | 5.0 | NaN | NaN | 3.926822 | NaN | 2.866472 | 5.000000 | NaN | 2.000000 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 2018-02-25 17:47:12 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Alex | 3.333333 | 2015-07-27 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | NaN | NaN | 4.0 | NaN | 5.000000 | 2.5 | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | NaN | 5.000000 | 2.5 | NaN | NaN | NaN | NaN | NaN | NaN | 4.000000 | NaN | 5.000000 | 2.353400 | NaN | NaN | NaN | NaN |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 2018-05-06 04:22:48 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Mermaid | 2.571429 | 2012-01-01 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.0 | 3.0 | NaN | 3.200000 | NaN | NaN | 1.0 | 2.333333 | NaN | 2.500000 | 1.0 | 3.0 | NaN | 3.200000 | NaN | NaN | 1.0 | 3.0 | NaN | 2.505665 | 1.0 | 2.990709 | NaN | 3.204491 | NaN | NaN | 1.000000 | 2.974502 | NaN |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 2018-04-22 17:42:09 | 1 | 0.988395 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | The Spicy Amigos | 821 4 Avenue SW | Mexican | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | NaN | False | NaN | NaN | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | NaN | Jen | 3.703313 | 2013-02-27 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 2018-05-21 05:09:07 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.0 | Alex | 2.980769 | 2008-11-15 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.0 | NaN | 2.0 | 3.040000 | 2.6 | 3.6 | 2.5 | 1.000000 | 3.75 | 2.923077 | 4.0 | NaN | 1.666667 | 3.146341 | 2.6 | 3.75 | 2.5 | 1.0 | 3.666667 | 2.954478 | 4.0 | NaN | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 |
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set.pickle')
test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | date | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | name | address | cuisine | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | user_name | average_stars_user | yelping_since | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 2018-10-21 18:45:39 | 1 | 0.997555 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Fran | 3.703313 | 2017-01-09 | 67 | 1 | 1 | 16 | 6 | 2 | 3574 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-21 01:07:38 | -1 | 0.553523 | 1.8 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Leung | 2.875000 | 2016-07-20 | 86 | 0 | 1 | 34 | 9 | 12 | 166 | 16.0 | 3.000000 | 15.0 | 2.909282 | 13.563981 | 1.8 | 2.20 | NaN | 4.5 | 3.333333 | 1.000000 | 3.0 | NaN | NaN | NaN | 2.000000 | 2.50 | NaN | 4.5 | 3.333333 | 1.0 | 3.0 | NaN | NaN | NaN | 1.799679 | 2.460018 | NaN | 4.571695 | 3.355656 | 1.000000 | 2.924220 | NaN | NaN | NaN |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 2018-09-09 03:20:03 | 1 | 0.990602 | 4.3 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Jo | 4.108108 | 2017-08-08 | 227 | 2 | 7 | 99 | 47 | 30 | 286 | 37.0 | 4.161290 | 31.0 | 4.130733 | 31.326167 | 4.3 | 3.75 | NaN | 3.5 | 4.454545 | 3.666667 | 3.8 | NaN | 4.0 | 4.0 | 4.333333 | 3.75 | NaN | 3.0 | 4.555556 | 3.5 | 4.0 | NaN | 4.0 | 4.0 | 4.299574 | 3.724926 | NaN | 3.340936 | 4.538601 | 3.626374 | 3.946442 | NaN | 4.0 | 4.0 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 2018-10-04 01:37:05 | 1 | 0.968214 | NaN | NaN | NaN | NaN | NaN | NaN | 0 | John's Chinese BBQ Restaurant | 328 Highway 7 E, Chalmers Gate 11, Unit 10 | Chinese | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | NaN | Cindy | 3.703313 | 2016-03-09 | 1 | 0 | 0 | 0 | 0 | 0 | 2110 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 2018-11-06 19:48:01 | 1 | 0.995667 | NaN | NaN | NaN | NaN | NaN | NaN | 1 | Delmonico Steakhouse | 3355 Las Vegas Blvd S | Others | 89109 | 36.123183 | -115.169190 | 1613 | 4.0 | False | True | False | True | No | Full_Bar | Cajun/Creole, Seafood, Steakhouses, Restaurants | Las Vegas | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 17:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:00:00 | 22:30:00 | 22:30:00 | 22:00:00 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | NaN | Katie | 3.703313 | 2017-06-15 | 59 | 0 | 0 | 5 | 0 | 1 | 22 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
train_test_set = _pd.concat([train_set, test_set], sort=False)
print("train size:", train_set.shape)
print("test size:", test_set.shape)
print("train_test size:", train_test_set.shape)
print(train_set.shape[0] + test_set.shape[0] == train_test_set.shape[0])
_train_len = train_set.shape[0]
train size: (558386, 99) test size: (153993, 99) train_test size: (712379, 99) True
Drop useless features
train_test_set.drop(columns=['date', 'name', 'address', 'yelping_since', 'user_name', 'cuisine'], inplace=True)
Fill missing values
train_test_set['OutdoorSeating'] = train_test_set['OutdoorSeating'].fillna('None')
train_test_set['BusinessAcceptsCreditCards'] = train_test_set['BusinessAcceptsCreditCards'].fillna('None')
train_test_set['RestaurantsDelivery'] = train_test_set['RestaurantsDelivery'].fillna('None')
train_test_set['RestaurantsReservations'] = train_test_set['RestaurantsReservations'].fillna('None')
train_test_set['WiFi'] = train_test_set['WiFi'].fillna('None')
train_test_set['Alcohol'] = train_test_set['Alcohol'].fillna('None')
train_test_set['Monday_Open'] = train_test_set["Monday_Open"].astype(str)
train_test_set['Monday_Open'] = train_test_set['Monday_Open'].fillna(train_test_set['Monday_Open'].mode())
train_test_set['Tuesday_Open'] = train_test_set["Tuesday_Open"].astype(str)
train_test_set['Tuesday_Open'] = train_test_set['Tuesday_Open'].fillna(train_test_set['Tuesday_Open'].mode())
train_test_set['Wednesday_Open'] = train_test_set["Wednesday_Open"].astype(str)
train_test_set['Wednesday_Open'] = train_test_set['Wednesday_Open'].fillna(train_test_set['Wednesday_Open'].mode())
train_test_set['Thursday_Open'] = train_test_set["Thursday_Open"].astype(str)
train_test_set['Thursday_Open'] = train_test_set['Thursday_Open'].fillna(train_test_set['Thursday_Open'].mode())
train_test_set['Friday_Open'] = train_test_set["Friday_Open"].astype(str)
train_test_set['Friday_Open'] = train_test_set['Friday_Open'].fillna(train_test_set['Friday_Open'].mode())
train_test_set['Saturday_Open'] = train_test_set["Saturday_Open"].astype(str)
train_test_set['Saturday_Open'] = train_test_set['Saturday_Open'].fillna(train_test_set['Saturday_Open'].mode())
train_test_set['Sunday_Open'] = train_test_set["Sunday_Open"].astype(str)
train_test_set['Sunday_Open'] = train_test_set['Sunday_Open'].fillna(train_test_set['Sunday_Open'].mode())
train_test_set['Monday_Close'] = train_test_set["Monday_Close"].astype(str)
train_test_set['Monday_Close'] = train_test_set['Monday_Close'].fillna(train_test_set['Monday_Close'].mode())
train_test_set['Tuesday_Close'] = train_test_set["Tuesday_Close"].astype(str)
train_test_set['Tuesday_Close'] = train_test_set['Tuesday_Close'].fillna(train_test_set['Tuesday_Close'].mode())
train_test_set['Wednesday_Close'] = train_test_set["Wednesday_Close"].astype(str)
train_test_set['Wednesday_Close'] = train_test_set['Wednesday_Close'].fillna(train_test_set['Wednesday_Close'].mode())
train_test_set['Thursday_Close'] = train_test_set["Thursday_Close"].astype(str)
train_test_set['Thursday_Close'] = train_test_set['Thursday_Close'].fillna(train_test_set['Thursday_Close'].mode())
train_test_set['Friday_Close'] = train_test_set["Friday_Close"].astype(str)
train_test_set['Friday_Close'] = train_test_set['Friday_Close'].fillna(train_test_set['Friday_Close'].mode())
train_test_set['Saturday_Close'] = train_test_set["Saturday_Close"].astype(str)
train_test_set['Saturday_Close'] = train_test_set['Saturday_Close'].fillna(train_test_set['Saturday_Close'].mode())
train_test_set['Sunday_Close'] = train_test_set["Sunday_Close"].astype(str)
train_test_set['Sunday_Close'] = train_test_set['Sunday_Close'].fillna(train_test_set['Sunday_Close'].mode())
for ind, dtype in train_test_types.iteritems():
if _np.issubdtype(dtype, _np.floating):
train_test_set[ind] = train_test_set[ind].fillna(train_test_set[ind].mean())
elif _np.issubdtype(dtype, _np.integer):
train_test_set[ind] = train_test_set[ind].fillna(round(train_test_set[ind].mean()))
# check any feature still has null values
train_test_set.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 712379 entries, 0 to 153992 Data columns (total 93 columns): review_id 712379 non-null object user_id 712379 non-null object business_id 712379 non-null object stars_review 712379 non-null int64 useful_review 712379 non-null int64 funny_review 712379 non-null int64 cool_review 712379 non-null int64 bin_truth_score 712379 non-null int64 real_truth_score 712379 non-null float64 cuisine_av_hist 712379 non-null float64 cuisine_av_hist_bin 712379 non-null float64 cuisine_av_hist_real 712379 non-null float64 coll_score 712379 non-null float64 coll_score_bin 712379 non-null float64 coll_score_real 712379 non-null float64 likes 712379 non-null int32 postal_code 712379 non-null object latitude 712379 non-null float64 longitude 712379 non-null float64 review_count 712379 non-null int64 stars_restaurant 712379 non-null float64 OutdoorSeating 712379 non-null object BusinessAcceptsCreditCards 712379 non-null object RestaurantsDelivery 712379 non-null object RestaurantsReservations 712379 non-null object WiFi 712379 non-null object Alcohol 712379 non-null object categories 712379 non-null object city 712379 non-null object Monday_Open 712379 non-null object Tuesday_Open 712379 non-null object Wednesday_Open 712379 non-null object Thursday_Open 712379 non-null object Friday_Open 712379 non-null object Saturday_Open 712379 non-null object Sunday_Open 712379 non-null object Monday_Close 712379 non-null object Tuesday_Close 712379 non-null object Wednesday_Close 712379 non-null object Thursday_Close 712379 non-null object Friday_Close 712379 non-null object Saturday_Close 712379 non-null object Sunday_Close 712379 non-null object average_stars_review 712379 non-null float64 num_reviews_review 712379 non-null float64 average_stars_bin_review 712379 non-null float64 num_reviews_bin_review 712379 non-null float64 average_stars_real_review 712379 non-null float64 num_reviews_real_review 712379 non-null float64 compliment_count 712379 non-null float64 average_stars_user 712379 non-null float64 review 712379 non-null int64 years_of_elite 712379 non-null int64 fans 712379 non-null int64 useful_user 712379 non-null int64 cool_user 712379 non-null int64 funny_user 712379 non-null int64 friends 712379 non-null int64 num_reviews_user 712379 non-null float64 average_stars_bin_user 712379 non-null float64 num_reviews_bin_user 712379 non-null float64 average_stars_real_user 712379 non-null float64 num_reviews_real_user 712379 non-null float64 av_rat_chinese_cuisine 712379 non-null float64 av_rat_japanese_cuisine 712379 non-null float64 av_rat_mexican_cuisine 712379 non-null float64 av_rat_italian_cuisine 712379 non-null float64 av_rat_others_cuisine 712379 non-null float64 av_rat_american_cuisine 712379 non-null float64 av_rat_korean_cuisine 712379 non-null float64 av_rat_mediterranean_cuisine 712379 non-null float64 av_rat_thai_cuisine 712379 non-null float64 av_rat_asianfusion_cuisine 712379 non-null float64 av_rat_chinese_cuisine_bin 712379 non-null float64 av_rat_japanese_cuisine_bin 712379 non-null float64 av_rat_mexican_cuisine_bin 712379 non-null float64 av_rat_italian_cuisine_bin 712379 non-null float64 av_rat_others_cuisine_bin 712379 non-null float64 av_rat_american_cuisine_bin 712379 non-null float64 av_rat_korean_cuisine_bin 712379 non-null float64 av_rat_mediterranean_cuisine_bin 712379 non-null float64 av_rat_thai_cuisine_bin 712379 non-null float64 av_rat_asianfusion_cuisine_bin 712379 non-null float64 av_rat_chinese_cuisine_real 712379 non-null float64 av_rat_japanese_cuisine_real 712379 non-null float64 av_rat_mexican_cuisine_real 712379 non-null float64 av_rat_italian_cuisine_real 712379 non-null float64 av_rat_others_cuisine_real 712379 non-null float64 av_rat_american_cuisine_real 712379 non-null float64 av_rat_korean_cuisine_real 712379 non-null float64 av_rat_mediterranean_cuisine_real 712379 non-null float64 av_rat_thai_cuisine_real 712379 non-null float64 av_rat_asianfusion_cuisine_real 712379 non-null float64 dtypes: float64(53), int32(1), int64(13), object(26) memory usage: 508.2+ MB
Convert non-numerical features
We print and plot the distribution of the cities, to see the long tail and decide how many of them to keep.
city_count = train_test_set['city'].value_counts()
print(city_count.to_string())
print(city_count.shape)
Las Vegas 208437 Phoenix 71126 Toronto 57047 Charlotte 40190 Scottsdale 36579 Pittsburgh 26891 Henderson 21518 Montréal 18531 Tempe 18354 Mesa 17235 Chandler 15441 Gilbert 14055 Cleveland 12873 Glendale 10742 Madison 9688 Markham 8248 Calgary 7958 Mississauga 7192 Peoria 6582 North Las Vegas 4771 Surprise 4117 Richmond Hill 3886 Goodyear 3020 Concord 2905 Scarborough 2544 Champaign 2513 Vaughan 2496 Avondale 2451 Huntersville 2046 Lakewood 2008 Matthews 1986 North York 1977 Fort Mill 1851 Brampton 1643 Cave Creek 1538 Gastonia 1282 Cornelius 1278 Etobicoke 1229 Akron 1188 Westlake 1118 Boulder City 1059 Thornhill 985 Strongsville 957 Middleton 930 Mentor 864 Fountain Hills 836 Cuyahoga Falls 828 Rock Hill 820 Cleveland Heights 813 Parma 789 Kent 745 Litchfield Park 745 Aurora 712 North Olmsted 693 Beachwood 654 Independence 648 Pineville 647 Medina 647 Newmarket 637 Belmont 631 Monroeville 610 Willoughby 607 Rocky River 579 Solon 576 Oakville 573 Indian Trail 545 Avon 543 Bridgeville 512 Davidson 507 Hudson 500 Pickering 493 Laval 491 Urbana 491 Ajax 479 Fitchburg 464 Waxhaw 462 Montreal 441 Sun Prairie 434 Homestead 429 Paradise Valley 425 Chagrin Falls 410 Stow 406 Canonsburg 400 Woodbridge 374 Wexford 368 Whitby 367 Woodmere 360 Carnegie 358 Lyndhurst 358 Coraopolis 341 Fairlawn 341 Bethel Park 331 Unionville 326 Elyria 319 Verona 315 Berea 313 Spring Valley 308 Sewickley 306 Orange Village 297 Carefree 294 Denver 288 Brossard 284 Brunswick 282 Sun City 280 Indian Land 267 Broadview Heights 259 Fairview Park 254 Monona 253 Laveen 253 Harrisburg 252 Avon Lake 249 Tolleson 243 Twinsburg 243 Mayfield Heights 242 Buckeye 242 Middleburg Heights 239 Macedonia 238 Dollard-des-Ormeaux 238 Brooklyn 233 Oakmont 224 Mint Hill 224 Painesville 223 North Royalton 210 Kannapolis 204 Amherst 203 Queen Creek 203 Streetsboro 196 Monroe 194 Clover 193 El Mirage 192 Orange 188 Murrysville 188 Mount Holly 185 Verdun 170 Dorval 168 Maple 167 Lake Wylie 167 Chardon 166 University Heights 165 Valley View 153 Highland Heights 153 South Las Vegas 153 Airdrie 150 York 149 Tega Cay 148 Lorain 147 West Mifflin 146 McMurray 144 Moon Township 144 Chesterland 141 Euclid 141 Allison Park 140 Irwin 139 Brecksville 138 Westmount 138 Waunakee 137 Brook Park 137 Stoughton 134 Pointe-Claire 130 Northfield 127 Warrensville Heights 127 Saint-Laurent 126 South Euclid 126 East York 124 McKees Rocks 120 Bellevue 117 Olmsted Falls 116 Braddock 116 Blue Diamond 109 Ahwatukee 109 New Kensington 108 Seven Hills 107 Mooresville 106 Upper Saint Clair 105 Longueuil 105 North Ridgeville 102 West Homestead 98 Eastlake 97 Oregon 97 Gibsonia 96 Sun City West 92 Anthem 90 Willoughby Hills 90 Pleasant Hills 90 Youngtown 88 Willowick 88 DeForest 86 Parma Heights 84 Upper St Clair 83 Tallmadge 82 Stouffville 81 Bedford 80 Las Vegas 79 Robinson Township 79 Whitchurch-Stouffville 77 LasVegas 77 Peninsula 76 Saint-Sauveur 75 Stallings 74 Bolton 73 Trafford 72 Glenshaw 72 Mount Horeb 71 Wesley Chapel 70 Moreland Hills 70 Pierrefonds 69 Missisauga 68 Higley 67 Heidelberg 67 Millvale 66 Vaudreuil-Dorion 65 Lachine 64 N. Las Vegas 64 Wickliffe 62 Ambridge 62 Finleyville 62 Burton 61 Munroe Falls 61 Brentwood 59 Shaker Heights 59 Tarentum 59 McKeesport 59 Sun Lakes 58 Georgetown 57 Rantoul 57 Bay Village 57 East Gwillimbury 56 Cecil 56 Wadsworth 56 Lasalle 56 Richfield 55 Mt. Lebanon 55 Kirkland 55 Mc Kees Rocks 54 Oakdale 54 North Versailles 54 Mayfield 53 MESA 53 King City 52 Blakeney 51 Enterprise 50 Ross Township 50 Cramerton 50 Mahomet 49 North Randall 49 Pepper Pike 48 McAdenville 48 Cottage Grove 47 Wilkinsburg 47 Outremont 47 lyndhurst 46 Savoy 45 Grafton 45 Turtle Creek 45 Maple Heights 45 Lowell 44 Springdale 44 Concord Mills 44 Valley City 43 Richmond Heights 43 White Oak 43 Bradford 43 Bedford Heights 42 West View 42 Dallas 42 GILBERT 42 Moon 41 L'Île-Perrot 40 Grand River 40 Monticello 40 Sheffield Village 40 Hampton Township 39 Rexdale 39 Lower Lawrenceville 39 Guadalupe 38 McFarland 38 Bradford West Gwillimbury 37 North Huntingdon 37 Copley 36 Caledon 36 Laveen Village 36 Summerlin 35 Fairport Harbor 35 Gates Mills 35 Elizabeth 34 Mentor-on-the-Lake 34 Saint-Hubert 33 Mayfield Village 33 Boucherville 33 De Forest 33 Apache Junction 33 Windsor 33 Kirtland 33 Locust 33 Munhall 32 Penn Hills 32 Saint-Lambert 31 Leetsdale 31 Harmarville 31 Tremont 31 Aspinwall 31 Norton 30 Tornto 30 Crafton 29 Garfield Heights 29 Robinson 28 Weddington 27 Sainte-Anne-de-Bellevue 27 Saint-Léonard 27 Plum 26 Woodmere Village 26 Blawnox 26 La Prairie 26 Castle Shannon 26 Rio Verde 26 Fort McDowell 25 Middlefield 25 Scottdale 24 charlotte 24 Newbury 23 Sharpsburg 23 Robinson Twp. 23 Cross Plains 23 Mount Lebanon 23 N Las Vegas 22 Port Credit 22 Rocky View 22 Glendale Az 22 Saint-Jérôme 22 Saint-Jean-sur-Richelieu 22 Emsworth 21 Lower Burrell 21 Clairton 21 Mont-Royal 21 Stanley 21 Chambly 21 Montrose 21 Cheswick 20 Etna 20 Chestermere 19 Harrison City 19 Mc Murray 19 Walton Hills 19 Halton Hills 19 Monongahela 18 Midland 18 South Park 18 Anjou 18 North Olmstead 17 Blainville 17 Greenfield Park 17 Dollard-Des-Ormeaux 16 Brooklin 16 Ogden 16 Mt Lebanon 16 Imperial 16 St-Benoît de Mirabel 15 LaSalle 15 Baldwin 15 St Joseph 15 Ranlo 15 Montreal-Nord 15 Export 15 LAS VEGAS 15 Richmond Hil 15 Don Mills 14 Columbia Station 14 Mc Farland 14 Nellis AFB 14 Kleinburg 14 Brookpark 14 McCandless Township 14 New Eagle 13 Olmsted Township 13 Rouses Point 13 Sainte-Thérèse 13 Phx 13 Boisbriand 13 Saint-Lazare 12 Glassport 12 Dollard-des Ormeaux 12 Peters Township 12 Montréal-Ouest 12 Harrisbug 12 Green Tree 11 Saint-Eustache 11 Elizabeth Township 11 Sagamore Hills 11 Mc Donald 11 Indian land 11 Lower burrell 11 Presto 11 Jefferson Hills 11 Sainte-Anne-De-Bellevue 11 Terrebonne 11 South Park Township 11 Waddell 11 Avalon 11 Downtown 11 Etibicoke 10 Bainbridge 10 Beaconsfield 10 Rosemère 10 Saint-Hyacinthe 10 Sainte-Adèle 10 East Liberty 10 La Salle 10 PHOENIX 10 Russellton 10 Mont-Saint-Hilaire 10 Lagrange 10 Mint Hill 10 Lachute 9 Swissvale 9 Medina Township 9 Mentor On The Lake 9 East Ajax 9 Neville Island 9 Paradise 9 Forest Hills 9 North York 9 Warrensville Hts. 9 Mantua 9 Châteauguay 8 Hinckley 8 N Ridgeville 8 Schomberg 8 Moseley 8 Balzac 8 Tuscola 8 Wilmerding 8 Oakland 8 East Cleveland 8 Bainbridge Township 8 North Toronto 7 Montréal-Nord 7 Montreal-West 7 Belle Vernon 7 Nobleton 7 Central 7 Belleville 7 Willowdale 7 St-Jerome 7 Mont-Saint-Grégoire 7 Moon TWP 7 Vimont 7 Rigaud 7 Mascouche 7 Milton 7 Sheffield 7 Library 7 Ville Mont-Royal 7 Glen Williams 7 Lowesville 7 Ben Avon 6 Sunrise Manor 6 Sheffield Lake 6 solon 6 Brunswick Hills 6 Cuddy 6 Goodwood 6 Shaler Township 6 Sainte-Adele 6 Whiitby 6 Venetia 6 Indianola 6 Arnold 6 Saintt-Bruno-de-Montarville 6 Thornhil 5 Duquesne 5 Chateauguay 5 Wilkins Township 5 Dane 5 Communauté-Urbaine-de-Montréal 5 SCOTTSDALE 5 GOODYEAR 5 Bellvue 5 Herminie 5 Mt Holly 5 Omaha 5 Kahnawake 5 Bath 5 Sturgeon 5 Troy Township 5 Sainte-Genevieve 5 Saint-Leonard 5 Mckees Rocks 5 McCandless 5 TORONTO 5 Mesa AZ 5 St-Leonard 5 Mt. Holly 5 Auburn Township 4 Chargrin Falls 4 Sainte-Julie 4 NELLIS AFB 4 Deforest 4 Saint-Marc-sur-Richelieu 4 Ravenna 4 Dravosburg 4 Mcfarland 4 ETOBICOKE 4 St Leonard 4 King 4 Mcknight 4 Uxbridge 4 L'ile-Perrot 4 Fort Mcdowell 4 Saint-Bruno-de-Montarville 4 Beeton 4 Saint-Jerome 4 Elrama 4 Litchfield 4 Saint Joseph 4 Dollard-Des Ormeaux 4 St-Bruno-de-Montarville 4 L'Île-Bizard 4 Scarobrough 4 Lawrence 4 Saint-Constant 4 Lindale 4 Beloeil 4 Repentigny 4 Estérel 4 Mcmurray 4 Homer 3 Delson 3 McDonald 3 Morin-Heights 3 Central City 3 South Amherst 3 Painesville Township 3 Canonsburd 3 Mount Albert 3 Paoli 3 Nellis Air Force Base 3 Northwest Calgary 3 Oakwood Village 3 Central City Village 3 Concord Township 3 Rostraver 3 Mirabel 3 Saint-Henri 3 Fisher 3 Harwick 3 Mooers 3 Rural Ridge 3 verdun 3 Champlain 3 Lake Park 3 River Drive Park 3 Etobiicoke 3 Montgomery 3 East Mc Keesport 3 Marshall 3 Gifford 2 Auburn Twp 2 Northfield Center Township 2 Mount Oliver 2 Mount Washington 2 Midnapore 2 Pointe-Calumet 2 Creighton 2 Mentor On the Lake 2 Hyland Heights 2 Saint Laurent 2 Holland Landing 2 Palgrave 2 kirtland 2 Saint-Basile-Le-Grand 2 Schottsdale 2 Pointe-Aux-Trembles 2 Venise-en-Québec 2 Sainte-Marguerite-Esterel 2 Richmond Hts 2 Thorncliffe Park 2 Toronto Scarborough 2 Rillton 2 Val-Morin 2 Ross 2 Clark 2 Penn Hills Township 2 East Pittsburgh 2 Bois-des-Filion 2 Joliette 2 Saint-Basile-le-Grand 2 Cleveland Hghts. 2 Hendersonville 2 Mckeesport 2 Sauk City 2 Pincourt 2 Brookline 2 Clarkson 2 Rocky View No. 44 2 THORNHILL 2 Waterloo 2 Candiac 2 Sainte-Thérèse-de-Blainville 2 Saint-Hippolyte 2 Rosemere 2 Hemmingford 2 Montréal-West 1 North Braddock 1 Oakwood 1 Hampstead 1 Tottenham 1 Saint-laurent 1 Salaberry-De-Valleyfield 1 Caledon Village 1 Henryville 1 Les Coteaux 1 Cote Saint-Luc 1 Port Vue 1 Sainte-Marthe 1 Aliquippa 1 Laval, Ste Dorothee 1 Sainte-Catherine 1 East McKeesport 1 Sainte-Marguerite-du-lac-Masson 1 North Huntington 1 Pitcairn 1 Cuyahoga Fls 1 Québec 1 Saint-Bruno 1 Lawrenceville 1 Salaberry-de-Valleyfield 1 Caledon East 1 Inglewood 1 Fabreville 1 Shorewood Hills 1 Midway 1 Edgewood 1 cave creek 1 Tolono 1 Malton 1 Beauharnois 1 Ange-Gardien 1 West Elizabeth 1 Richmond Height 1 Brownsburg-Chatham 1 Iberville 1 Montreal-Est 1 Oak Ridges 1 Rocky View County 1 Kennedy Township 1 Huntingdon 1 Bratenahl 1 Napierville 1 Plum Boro 1 De Winton 1 St. Léonard 1 Mississuaga 1 Buena Vista 1 Rawdon 1 Black Earth 1 North Strabane Township 1 L'Assomption 1 Ste-Rose 1 springdale 1 Charlemagne 1 Les Cèdres 1 Agincourt 1 Île-des-Soeurs 1 Mercier 1 Chatauguay 1 Sun Praiie 1 LaGrange 1 Coteau-du-Lac 1 Deerfield 1 Cuyahoga Heights 1 Paw Creek 1 (671,)
data = train_test_set.loc[train_test_set['city']!="Las Vegas", 'city']
weights = _np.ones(len(data)) / len(data)
_plt.figure(figsize=(20,10))
_plt.hist(data, weights=weights, bins=100)
_plt.title("City distribution")
_plt.gca().yaxis.set_major_formatter(_PercentFormatter(1))
_plt.show()
main_cities = city_count.where(city_count >= 100).dropna()
print(main_cities.to_string())
print(main_cities.shape)
main_cities = '|'.join(list(main_cities.index))
Las Vegas 208437.0 Phoenix 71126.0 Toronto 57047.0 Charlotte 40190.0 Scottsdale 36579.0 Pittsburgh 26891.0 Henderson 21518.0 Montréal 18531.0 Tempe 18354.0 Mesa 17235.0 Chandler 15441.0 Gilbert 14055.0 Cleveland 12873.0 Glendale 10742.0 Madison 9688.0 Markham 8248.0 Calgary 7958.0 Mississauga 7192.0 Peoria 6582.0 North Las Vegas 4771.0 Surprise 4117.0 Richmond Hill 3886.0 Goodyear 3020.0 Concord 2905.0 Scarborough 2544.0 Champaign 2513.0 Vaughan 2496.0 Avondale 2451.0 Huntersville 2046.0 Lakewood 2008.0 Matthews 1986.0 North York 1977.0 Fort Mill 1851.0 Brampton 1643.0 Cave Creek 1538.0 Gastonia 1282.0 Cornelius 1278.0 Etobicoke 1229.0 Akron 1188.0 Westlake 1118.0 Boulder City 1059.0 Thornhill 985.0 Strongsville 957.0 Middleton 930.0 Mentor 864.0 Fountain Hills 836.0 Cuyahoga Falls 828.0 Rock Hill 820.0 Cleveland Heights 813.0 Parma 789.0 Kent 745.0 Litchfield Park 745.0 Aurora 712.0 North Olmsted 693.0 Beachwood 654.0 Independence 648.0 Pineville 647.0 Medina 647.0 Newmarket 637.0 Belmont 631.0 Monroeville 610.0 Willoughby 607.0 Rocky River 579.0 Solon 576.0 Oakville 573.0 Indian Trail 545.0 Avon 543.0 Bridgeville 512.0 Davidson 507.0 Hudson 500.0 Pickering 493.0 Laval 491.0 Urbana 491.0 Ajax 479.0 Fitchburg 464.0 Waxhaw 462.0 Montreal 441.0 Sun Prairie 434.0 Homestead 429.0 Paradise Valley 425.0 Chagrin Falls 410.0 Stow 406.0 Canonsburg 400.0 Woodbridge 374.0 Wexford 368.0 Whitby 367.0 Woodmere 360.0 Carnegie 358.0 Lyndhurst 358.0 Coraopolis 341.0 Fairlawn 341.0 Bethel Park 331.0 Unionville 326.0 Elyria 319.0 Verona 315.0 Berea 313.0 Spring Valley 308.0 Sewickley 306.0 Orange Village 297.0 Carefree 294.0 Denver 288.0 Brossard 284.0 Brunswick 282.0 Sun City 280.0 Indian Land 267.0 Broadview Heights 259.0 Fairview Park 254.0 Monona 253.0 Laveen 253.0 Harrisburg 252.0 Avon Lake 249.0 Tolleson 243.0 Twinsburg 243.0 Mayfield Heights 242.0 Buckeye 242.0 Middleburg Heights 239.0 Macedonia 238.0 Dollard-des-Ormeaux 238.0 Brooklyn 233.0 Oakmont 224.0 Mint Hill 224.0 Painesville 223.0 North Royalton 210.0 Kannapolis 204.0 Amherst 203.0 Queen Creek 203.0 Streetsboro 196.0 Monroe 194.0 Clover 193.0 El Mirage 192.0 Orange 188.0 Murrysville 188.0 Mount Holly 185.0 Verdun 170.0 Dorval 168.0 Maple 167.0 Lake Wylie 167.0 Chardon 166.0 University Heights 165.0 Valley View 153.0 Highland Heights 153.0 South Las Vegas 153.0 Airdrie 150.0 York 149.0 Tega Cay 148.0 Lorain 147.0 West Mifflin 146.0 McMurray 144.0 Moon Township 144.0 Chesterland 141.0 Euclid 141.0 Allison Park 140.0 Irwin 139.0 Brecksville 138.0 Westmount 138.0 Waunakee 137.0 Brook Park 137.0 Stoughton 134.0 Pointe-Claire 130.0 Northfield 127.0 Warrensville Heights 127.0 Saint-Laurent 126.0 South Euclid 126.0 East York 124.0 McKees Rocks 120.0 Bellevue 117.0 Olmsted Falls 116.0 Braddock 116.0 Blue Diamond 109.0 Ahwatukee 109.0 New Kensington 108.0 Seven Hills 107.0 Mooresville 106.0 Upper Saint Clair 105.0 Longueuil 105.0 North Ridgeville 102.0 (176,)
train_test_set['city'] = train_test_set['city'].str.findall(main_cities)
train_test_set['city'] = train_test_set['city'].map(lambda x: 'Other' if x==[] else x[0])
train_test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.622302 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.846154 | 18 | 0 | 1 | 8 | 4 | 4 | 4 | 13.0 | 4.222222 | 9.0 | 3.798692 | 11.168551 | 3.556721 | 5.00000 | 3.686094 | 3.777956 | 3.857143 | 3.684951 | 3.000000 | 5.000000 | 3.868171 | 2.000000 | 3.542997 | 5.000000 | 3.662669 | 3.749776 | 5.000000 | 3.66752 | 3.000000 | 5.000000 | 3.851784 | 2.000000 | 3.555679 | 5.000000 | 3.678871 | 3.770170 | 3.926822 | 3.676024 | 2.866472 | 5.000000 | 3.867280 | 2.000000 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.333333 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | 3.556721 | 3.79608 | 4.000000 | 3.777956 | 5.000000 | 2.500000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 4.000000 | 3.749776 | 5.000000 | 2.50000 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 4.000000 | 3.770170 | 5.000000 | 2.353400 | 3.788204 | 3.928912 | 3.867280 | 3.767263 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 2.571429 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.00000 | 3.000000 | 3.777956 | 3.200000 | 3.684951 | 3.789846 | 1.000000 | 2.333333 | 3.770015 | 2.500000 | 1.000000 | 3.000000 | 3.749776 | 3.200000 | 3.66752 | 3.771654 | 1.000000 | 3.000000 | 3.744434 | 2.505665 | 1.000000 | 2.990709 | 3.770170 | 3.204491 | 3.676024 | 3.788204 | 1.000000 | 2.974502 | 3.767263 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 1 | 0.988395 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Restaurants, Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.703313 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese, Restaurants | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.000000 | 2.980769 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.00000 | 3.686094 | 2.000000 | 3.040000 | 2.600000 | 3.600000 | 2.500000 | 1.000000 | 3.750000 | 2.923077 | 4.000000 | 3.662669 | 1.666667 | 3.146341 | 2.60000 | 3.750000 | 2.500000 | 1.000000 | 3.666667 | 2.954478 | 4.000000 | 3.678871 | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 |
We print and plot the distribution of the categories, to see the long tail and decide how many of them to keep.
category_count = _pd.Series(', '.join(list(train_test_set['categories'])).split(', ')).value_counts()
print(category_count.to_string())
print(category_count.shape)
Restaurants 712379 Food 192841 Nightlife 170259 Bars 165959 American (Traditional) 123975 Breakfast & Brunch 120310 American (New) 117437 Sandwiches 82264 Mexican 73726 Burgers 71598 Pizza 67538 Italian 62490 Seafood 57228 Japanese 53952 Salad 49388 Fast Food 46982 Event Planning & Services 45382 Coffee & Tea 43592 Chinese 42971 Asian Fusion 42576 Sushi Bars 42487 Cafes 40002 Steakhouses 37097 Desserts 36523 Cocktail Bars 36152 Barbeque 33004 Sports Bars 26918 Vegetarian 25616 Chicken Wings 24833 Vegan 23768 Wine Bars 23685 Caterers 23328 Mediterranean 22625 Specialty Food 21980 Thai 21702 Bakeries 21321 Wine & Spirits 21204 Beer 21204 Pubs 21076 Arts & Entertainment 20724 Diners 20452 Gastropubs 17953 Korean 17831 Gluten-Free 17217 Buffets 17133 Juice Bars & Smoothies 16826 Soup 16070 Noodles 15555 Lounges 15291 Delis 14963 Vietnamese 14470 Southern 14139 Comfort Food 13805 Tapas/Small Plates 13335 French 12767 Beer Bar 12567 Venues & Event Spaces 12524 Canadian (New) 12509 Indian 12502 Tacos 12369 Hotels & Travel 11840 Middle Eastern 11447 Ramen 11015 Greek 10833 Tex-Mex 10615 Latin American 10474 Hotels 10261 Hawaiian 9674 Ice Cream & Frozen Yogurt 9485 Breweries 9293 Casinos 9055 Tapas Bars 8057 Ethnic Food 7862 Chicken Shop 6779 Hot Dogs 6534 Cajun/Creole 6237 Food Delivery Services 6237 Halal 5951 Poke 5903 Music Venues 5663 Local Flavor 5617 Soul Food 5577 Shopping 5421 Caribbean 5311 Food Trucks 5295 Bagels 5287 Bubble Tea 5119 Dim Sum 5023 Grocery 4705 Active Life 4482 New Mexican Cuisine 4385 Taiwanese 4155 Creperies 4008 Fish & Chips 3903 Waffles 3781 Resorts 3552 Street Vendors 3490 Pakistani 3441 Spanish 3393 Donuts 3392 Tea Rooms 3247 Wraps 3114 British 3019 Dive Bars 2989 Filipino 2898 Cantonese 2686 Dance Clubs 2642 Karaoke 2635 Brazilian 2614 Cheesesteaks 2594 Hot Pot 2589 Irish 2566 Beauty & Spas 2503 Modern European 2479 Beer Gardens 2269 Food Stands 2191 Lebanese 2170 Live/Raw Food 2108 Smokehouse 2062 Szechuan 1985 Meat Shops 1940 Arcades 1925 African 1904 Party & Event Planning 1852 German 1814 Patisserie/Cake Shop 1805 Health Markets 1785 Poutineries 1727 Day Spas 1724 Imported Food 1700 Izakaya 1668 Brewpubs 1638 Coffee Roasteries 1622 Teppanyaki 1577 Brasseries 1461 Irish Pub 1460 Peruvian 1452 Pan Asian 1441 Food Court 1419 Internet Cafes 1415 Persian/Iranian 1402 Pasta Shops 1357 Turkish 1330 Acai Bowls 1317 Kebab 1315 Falafel 1275 Portuguese 1260 Chocolatiers & Shops 1258 Cuban 1257 Butcher 1251 Bistros 1237 Golf 1212 Seafood Markets 1194 Whiskey Bars 1173 Shopping Centers 1146 Farmers Market 1141 Automotive 1130 Do-It-Yourself Food 1122 Mongolian 1073 Amusement Parks 1066 Bowling 1056 Conveyor Belt Sushi 1053 Hookah Bars 1051 Cupcakes 1018 Fondue 1005 Gelato 1003 Polish 1003 Singaporean 965 Cinema 964 Shaved Ice 951 Custom Cakes 938 Malaysian 926 Cheese Shops 904 Afghan 900 Jazz & Blues 884 Argentine 870 Laotian 848 Fruits & Veggies 843 Performing Arts 812 Kosher 808 Flowers & Gifts 798 Organic Stores 789 Colombian 751 Venezuelan 739 Convenience Stores 735 Wedding Planning 721 Arabian 706 Japanese Curry 705 Himalayan/Nepalese 675 Belgian 654 Ethiopian 644 Fashion 642 Car Wash 641 Pool Halls 588 Kids Activities 584 Local Services 582 Home & Garden 569 Tuscan 551 Home Services 542 Candy Stores 522 Gas Stations 501 Eatertainment 489 Delicatessen 475 Professional Services 471 Russian 468 Adult Entertainment 467 Salvadoran 467 Gift Shops 459 Hair Salons 453 International Grocery 451 Health & Medical 429 Moroccan 426 Social Clubs 407 Cafeteria 381 Piano Bars 376 Pretzels 364 Hungarian 360 Playgrounds 352 Bed & Breakfast 351 Ethnic Grocery 349 Public Markets 345 Speakeasies 344 Beverage Store 341 Art Galleries 333 Hakka 326 Egyptian 324 Pets 323 Puerto Rican 310 Museums 307 Ukrainian 306 Macarons 304 Education 303 Wineries 301 Empanadas 300 Beer Garden 291 Dinner Theater 289 International 288 Shaved Snow 284 Nail Salons 283 Arts & Crafts 280 Indonesian 265 Airports 265 Personal Chefs 263 Country Dance Halls 263 Outlet Stores 262 Musicians 259 Public Services & Government 249 Hong Kong Style Cafe 247 Real Estate 245 Hostels 245 Themed Cafes 241 Syrian 236 Donairs 236 Employment Agencies 232 Furniture Stores 231 Swimming Pools 229 Massage 228 Cambodian 213 Department Stores 213 Tours 211 Distilleries 198 Dominican 198 Mini Golf 195 Zoos 195 Hair Removal 191 Scandinavian 183 Armenian 179 Florists 178 Shanghainese 177 Festivals 173 Waxing 172 Drugstores 171 Kombucha 171 Cooking Classes 170 Tabletop Games 170 Eyelash Service 169 Personal Shopping 168 Bangladeshi 165 Nail Technicians 165 Botanical Gardens 165 Gay Bars 163 Appliances 160 Fitness & Instruction 158 Towing 157 Barbers 156 Wholesale Stores 154 Sri Lankan 154 Home Decor 152 Landmarks & Historical Buildings 151 Nutritionists 148 Kitchen & Bath 146 Community Service/Non-Profit 145 Basque 137 Tiki Bars 137 South African 127 Wedding Chapels 119 Books 117 Music & Video 117 Mags 117 Antiques 112 Sporting Goods 110 Animal Shelters 110 Skin Care 110 Stadiums & Arenas 109 Pop-Up Restaurants 106 Guamanian 106 Food Tours 105 Tobacco Shops 100 Cosmetics & Beauty Supply 99 Wine Tasting Room 98 Laser Tag 97 Herbs & Spices 97 Printing Services 94 Ethical Grocery 93 Screen Printing 93 Specialty Schools 93 Walking Tours 92 Recreation Centers 91 Cabaret 89 Burmese 89 Cideries 89 Udon 88 Coffee & Tea Supplies 78 Gyms 78 Champagne Bars 77 Street Art 76 Austrian 74 Baby Gear & Furniture 74 Wigs 74 Health Retreats 73 Australian 73 Building Supplies 72 Supper Clubs 72 Doctors 72 Couriers & Delivery Services 67 Czech 67 Tasting Classes 67 Auto Glass Services 66 Country Clubs 65 Yoga 65 Sports Wear 64 Religious Organizations 64 Horseback Riding 61 Mass Media 61 Shared Office Spaces 61 Team Building Activities 60 Hobby Shops 60 Shoe Stores 60 Virtual Reality Centers 60 Cooking Schools 59 Veterinarians 59 Pet Services 59 Art Classes 59 Haitian 59 Contractors 56 Wholesalers 56 Bartenders 54 Outdoor Furniture Stores 53 Appliances & Repair 52 Hospitals 52 Accessories 51 Parks 50 Uzbek 49 Financial Services 49 Comedy Clubs 48 Vehicle Wraps 47 Farms 47 Honduran 47 Art Museums 47 Car Window Tinting 47 Cultural Center 47 Vacation Rentals 47 Coffeeshops 46 Women's Clothing 45 Cigar Bars 45 DJs 45 Vitamins & Supplements 44 Dentists 44 Trinidadian 44 Trainers 43 Bookstores 43 Iberian 42 Emergency Medicine 40 Skating Rinks 40 Cardiologists 40 Jewelry 40 Pediatricians 40 Strip Clubs 39 Auto Repair 39 Sports Clubs 39 Party Equipment Rentals 39 Leisure Centers 39 Indoor Playcentre 39 Mattresses 38 Hotel bar 37 Colleges & Universities 37 Nicaraguan 37 Home Window Tinting 37 Transportation 37 Weight Loss Centers 36 Party Supplies 36 Pharmacy 36 Bar Crawl 36 Used 36 Vintage & Consignment 36 Airport Terminals 36 Men's Clothing 34 Vinyl Records 34 Cheese Tasting Classes 34 Limos 34 Wine Tasting Classes 33 Escape Games 32 Rock Climbing 32 Physical Therapy 32 Dry Cleaning & Laundry 32 Adult Education 31 Cannabis Clinics 31 RV Parks 31 Real Estate Services 31 Toy Stores 30 General Dentistry 30 Rehabilitation Center 30 Sicilian 30 CSA 29 Home Cleaning 29 Pet Adoption 28 Photography Stores & Services 28 Sugar Shacks 28 Trampoline Parks 28 Climbing 28 Cards & Stationery 27 Amateur Sports Teams 25 Plumbing 25 Popcorn Shops 25 Cannabis Dispensaries 25 Discount Store 25 Hair Stylists 25 Grilling Equipment 24 Currency Exchange 24 Cannabis Collective 24 Check Cashing/Pay-day Loans 24 Fireplace Services 23 Art Schools 22 Opera & Ballet 22 Security Systems 22 Leather Goods 22 Christmas Trees 21 Car Rental 21 Travel Services 21 Post Offices 20 Optometrists 20 Auto Parts & Supplies 20 Pet Stores 20 Transmission Repair 20 Bikes 20 Rotisserie Chicken 19 Electronics 19 Libraries 19 Oil Change Stations 19 Nurseries & Gardening 19 Body Shops 19 Auto Insurance 19 Windshield Installation & Repair 19 Insurance 19 Horse Racing 19 Massage Therapy 18 Videos & Video Game Rental 18 Family Practice 17 Golf Lessons 17 Makeup Artists 17 Airport Shuttles 16 Office Equipment 16 Pool & Billiards 16 Tennis 16 Bounce House Rentals 16 Laundry Services 16 Party Bus Rentals 16 Eyebrow Services 15 Dry Cleaning 15 Security Services 15 Race Tracks 15 Personal Assistants 15 Club Crawl 15 Skydiving 14 Sailing 14 Archery 14 Airport Lounges 14 Laser Hair Removal 14 Olive Oil 14 Tanning 14 Video Game Stores 13 Scottish 13 Tanning Beds 12 Pita 12 Interior Design 12 Used Bookstore 12 Spray Tanning 12 Bingo Halls 11 Yelp Events 11 Reflexology 11 Movers 11 Slovakian 11 Boat Charters 11 Town Car Service 11 Tires 11 Reunion 11 Septic Services 11 Lakes 11 Funeral Services & Cemeteries 11 Auto Customization 11 Drive-Thru Bars 11 Boating 11 Game Meat 11 Ice Delivery 11 Life Coach 11 Summer Camps 10 Soba 10 Golf Equipment 10 Eyewear & Opticians 10 Supernatural Readings 10 Signmaking 10 Psychics 10 Mobile Phones 10 Photographers 10 Paint & Sip 10 Ophthalmologists 10 Hair Extensions 9 Golf Cart Dealers 9 Historical Tours 9 Car Dealers 9 Meditation Centers 8 Oaxacan 8 Pick Your Own Farms 8 Business Consulting 8 Masonry/Concrete 8 Reiki 8 Comic Books 8 Psychologists 8 Commercial Truck Repair 8 Pumpkin Patches 8 Bike Rentals 8 Electricians 8 Photo Booth Rentals 8 Truck Rental 8 Counseling & Mental Health 8 Graphic Design 7 Mortgage Brokers 7 Recording & Rehearsal Studios 7 Bus Tours 7 Parenting Classes 7 Guest Houses 7 Tonkatsu 7 Preschools 7 Bike Repair/Maintenance 7 Head Shops 7 Restaurant Supplies 7 Ticket Sales 7 Tempura 7 Dance Schools 7 Real Estate Agents 7 Audio/Visual Equipment Rental 6 Art Supplies 6 Furniture Repair 6 Pilates 6 Trophy Shops 6 Souvenir Shops 6 Piercing 6 Traditional Clothing 6 Lighting Fixtures & Equipment 6 Bridal 6 Tattoo 6 Pool & Hot Tub Service 6 Fur Clothing 6 Beach Bars 6 Chiropractors 6 Martial Arts 6 Furniture Rental 6 Soccer 6 Carpet Installation 5 Handyman 5 Medical Spas 5 Pressure Washers 5 Gutter Services 5 Beaches 5 Acupuncture 5 Hot Tub & Pool 5 Tax Services 5 Window Washing 5 Windows Installation 5 Heating & Air Conditioning/HVAC 5 Golf Equipment Shops 5 Dog Walkers 5 Pet Sitting 5 Kids Hair Salons 5 Property Management 4 Door Sales/Installation 4 Gun/Rifle Ranges 4 Wine Tours 4 Bankruptcy Law 4 Bocce Ball 4 Honey 4 Glass & Mirrors 4 Lawyers 4 Hats 4 Knife Sharpening 4 Software Development 4 Tickets 4 Blow Dry/Out Services 4 Keys & Locksmiths 4 Water Stores 4 Holistic Animal Care 3 Calabrian 3 Animal Assisted Therapy 3 Animal Physical Therapy 3 Swiss Food 3 Aquarium Services 3 Local Fish Stores 3 Diagnostic Services 3 Brazilian Jiu-jitsu 3 Mauritius 3 Laboratory Testing 3 Thrift Stores 3 Cosmetic Dentists 3 Surf Schools 3 Tai Chi 3 Vape Shops 3 Private Tutors 3 Observatories 3 Ski Resorts 3 Web Design 3 Aquariums 3 Studio Taping 3 Churches 3 Advertising 3 Food Banks 3 Hardware Stores 3 Day Camps 3 Apartments 3 Occupational Therapy 3 Airsoft 3 Pet Training 3 Swimwear 3 Hainan 3 Accountants 3 Unofficial Yelp Events 3 Police Departments 3 Pop-up Shops 3 Air Duct Cleaning 3 Paint-Your-Own Pottery 3 Aircraft Repairs 3 Flea Markets 2 Pub Food 2 Divorce & Family Law 2 Engraving 2 Naturopathic/Holistic 2 Marinas 2 Plus Size Fashion 2 Immigration Law 2 Computers 2 Wills 2 Magicians 2 Medical Cannabis Referrals 2 Car Share Services 2 Bespoke Clothing 2 Public Transportation 2 Hunting & Fishing Supplies 2 Boat Dealers 2 Medical Centers 2 Trusts 2 Tax Law 2 Community Centers 2 Boat Repair 2 Outdoor Gear 2 Pet Boarding 2 Formal Wear 2 Marketing 2 Estate Planning Law 2 Painters 2 Courthouses 2 & Probates 2 Service Stations 2 Personal Injury Law 2 Drywall Installation & Repair 2 Clowns 2 Batting Cages 1 Beer Hall 1 Holiday Decorations 1 Furniture Reupholstery 1 Churros 1 Clothing Rental 1 University Housing 1 Pet Groomers 1 Auto Detailing 1 Pawn Shops 1 Senegalese 1 Gardeners 1 Campgrounds 1 Banks & Credit Unions 1 Laundromat 1 RV Repair 1 Roofing 1 Officiants 1 Music & DVDs 1 Home Health Care 1 Squash 1 Auto Upholstery 1 Siding 1 Landscaping 1 Motorcycle Repair 1 Ski Schools 1 Flooring 1 Taxis 1 Foundation Repair 1 Minho 1 (714,)
data = category_count.drop(labels=['Restaurants', 'Food']).index
vals = category_count.drop(labels=['Restaurants', 'Food']).values
weights = vals / vals.sum()
_plt.figure(figsize=(20,10))
_plt.hist(data, weights=weights, bins=100)
_plt.title("Category distribution")
_plt.gca().yaxis.set_major_formatter(_PercentFormatter(1))
_plt.show()
main_categories = category_count.drop(labels=['Restaurants', 'Food']).where(category_count >= 100).dropna()
print(main_categories.to_string())
print(main_categories.shape)
main_categories = '|'.join([_re.escape(x) for x in main_categories.index])
Nightlife 170259.0 Bars 165959.0 American (Traditional) 123975.0 Breakfast & Brunch 120310.0 American (New) 117437.0 Sandwiches 82264.0 Mexican 73726.0 Burgers 71598.0 Pizza 67538.0 Italian 62490.0 Seafood 57228.0 Japanese 53952.0 Salad 49388.0 Fast Food 46982.0 Event Planning & Services 45382.0 Coffee & Tea 43592.0 Chinese 42971.0 Asian Fusion 42576.0 Sushi Bars 42487.0 Cafes 40002.0 Steakhouses 37097.0 Desserts 36523.0 Cocktail Bars 36152.0 Barbeque 33004.0 Sports Bars 26918.0 Vegetarian 25616.0 Chicken Wings 24833.0 Vegan 23768.0 Wine Bars 23685.0 Caterers 23328.0 Mediterranean 22625.0 Specialty Food 21980.0 Thai 21702.0 Bakeries 21321.0 Wine & Spirits 21204.0 Beer 21204.0 Pubs 21076.0 Arts & Entertainment 20724.0 Diners 20452.0 Gastropubs 17953.0 Korean 17831.0 Gluten-Free 17217.0 Buffets 17133.0 Juice Bars & Smoothies 16826.0 Soup 16070.0 Noodles 15555.0 Lounges 15291.0 Delis 14963.0 Vietnamese 14470.0 Southern 14139.0 Comfort Food 13805.0 Tapas/Small Plates 13335.0 French 12767.0 Beer Bar 12567.0 Venues & Event Spaces 12524.0 Canadian (New) 12509.0 Indian 12502.0 Tacos 12369.0 Hotels & Travel 11840.0 Middle Eastern 11447.0 Ramen 11015.0 Greek 10833.0 Tex-Mex 10615.0 Latin American 10474.0 Hotels 10261.0 Hawaiian 9674.0 Ice Cream & Frozen Yogurt 9485.0 Breweries 9293.0 Casinos 9055.0 Tapas Bars 8057.0 Ethnic Food 7862.0 Chicken Shop 6779.0 Hot Dogs 6534.0 Cajun/Creole 6237.0 Food Delivery Services 6237.0 Halal 5951.0 Poke 5903.0 Music Venues 5663.0 Local Flavor 5617.0 Soul Food 5577.0 Shopping 5421.0 Caribbean 5311.0 Food Trucks 5295.0 Bagels 5287.0 Bubble Tea 5119.0 Dim Sum 5023.0 Grocery 4705.0 Active Life 4482.0 New Mexican Cuisine 4385.0 Taiwanese 4155.0 Creperies 4008.0 Fish & Chips 3903.0 Waffles 3781.0 Resorts 3552.0 Street Vendors 3490.0 Pakistani 3441.0 Spanish 3393.0 Donuts 3392.0 Tea Rooms 3247.0 Wraps 3114.0 British 3019.0 Dive Bars 2989.0 Filipino 2898.0 Cantonese 2686.0 Dance Clubs 2642.0 Karaoke 2635.0 Brazilian 2614.0 Cheesesteaks 2594.0 Hot Pot 2589.0 Irish 2566.0 Beauty & Spas 2503.0 Modern European 2479.0 Beer Gardens 2269.0 Food Stands 2191.0 Lebanese 2170.0 Live/Raw Food 2108.0 Smokehouse 2062.0 Szechuan 1985.0 Meat Shops 1940.0 Arcades 1925.0 African 1904.0 Party & Event Planning 1852.0 German 1814.0 Patisserie/Cake Shop 1805.0 Health Markets 1785.0 Poutineries 1727.0 Day Spas 1724.0 Imported Food 1700.0 Izakaya 1668.0 Brewpubs 1638.0 Coffee Roasteries 1622.0 Teppanyaki 1577.0 Brasseries 1461.0 Irish Pub 1460.0 Peruvian 1452.0 Pan Asian 1441.0 Food Court 1419.0 Internet Cafes 1415.0 Persian/Iranian 1402.0 Pasta Shops 1357.0 Turkish 1330.0 Acai Bowls 1317.0 Kebab 1315.0 Falafel 1275.0 Portuguese 1260.0 Chocolatiers & Shops 1258.0 Cuban 1257.0 Butcher 1251.0 Bistros 1237.0 Golf 1212.0 Seafood Markets 1194.0 Whiskey Bars 1173.0 Shopping Centers 1146.0 Farmers Market 1141.0 Automotive 1130.0 Do-It-Yourself Food 1122.0 Mongolian 1073.0 Amusement Parks 1066.0 Bowling 1056.0 Conveyor Belt Sushi 1053.0 Hookah Bars 1051.0 Cupcakes 1018.0 Fondue 1005.0 Gelato 1003.0 Polish 1003.0 Singaporean 965.0 Cinema 964.0 Shaved Ice 951.0 Custom Cakes 938.0 Malaysian 926.0 Cheese Shops 904.0 Afghan 900.0 Jazz & Blues 884.0 Argentine 870.0 Laotian 848.0 Fruits & Veggies 843.0 Performing Arts 812.0 Kosher 808.0 Flowers & Gifts 798.0 Organic Stores 789.0 Colombian 751.0 Venezuelan 739.0 Convenience Stores 735.0 Wedding Planning 721.0 Arabian 706.0 Japanese Curry 705.0 Himalayan/Nepalese 675.0 Belgian 654.0 Ethiopian 644.0 Fashion 642.0 Car Wash 641.0 Pool Halls 588.0 Kids Activities 584.0 Local Services 582.0 Home & Garden 569.0 Tuscan 551.0 Home Services 542.0 Candy Stores 522.0 Gas Stations 501.0 Eatertainment 489.0 Delicatessen 475.0 Professional Services 471.0 Russian 468.0 Adult Entertainment 467.0 Salvadoran 467.0 Gift Shops 459.0 Hair Salons 453.0 International Grocery 451.0 Health & Medical 429.0 Moroccan 426.0 Social Clubs 407.0 Cafeteria 381.0 Piano Bars 376.0 Pretzels 364.0 Hungarian 360.0 Playgrounds 352.0 Bed & Breakfast 351.0 Ethnic Grocery 349.0 Public Markets 345.0 Speakeasies 344.0 Beverage Store 341.0 Art Galleries 333.0 Hakka 326.0 Egyptian 324.0 Pets 323.0 Puerto Rican 310.0 Museums 307.0 Ukrainian 306.0 Macarons 304.0 Education 303.0 Wineries 301.0 Empanadas 300.0 Beer Garden 291.0 Dinner Theater 289.0 International 288.0 Shaved Snow 284.0 Nail Salons 283.0 Arts & Crafts 280.0 Indonesian 265.0 Airports 265.0 Personal Chefs 263.0 Country Dance Halls 263.0 Outlet Stores 262.0 Musicians 259.0 Public Services & Government 249.0 Hong Kong Style Cafe 247.0 Real Estate 245.0 Hostels 245.0 Themed Cafes 241.0 Syrian 236.0 Donairs 236.0 Employment Agencies 232.0 Furniture Stores 231.0 Swimming Pools 229.0 Massage 228.0 Cambodian 213.0 Department Stores 213.0 Tours 211.0 Distilleries 198.0 Dominican 198.0 Mini Golf 195.0 Zoos 195.0 Hair Removal 191.0 Scandinavian 183.0 Armenian 179.0 Florists 178.0 Shanghainese 177.0 Festivals 173.0 Waxing 172.0 Drugstores 171.0 Kombucha 171.0 Cooking Classes 170.0 Tabletop Games 170.0 Eyelash Service 169.0 Personal Shopping 168.0 Bangladeshi 165.0 Nail Technicians 165.0 Botanical Gardens 165.0 Gay Bars 163.0 Appliances 160.0 Fitness & Instruction 158.0 Towing 157.0 Barbers 156.0 Wholesale Stores 154.0 Sri Lankan 154.0 Home Decor 152.0 Landmarks & Historical Buildings 151.0 Nutritionists 148.0 Kitchen & Bath 146.0 Community Service/Non-Profit 145.0 Basque 137.0 Tiki Bars 137.0 South African 127.0 Wedding Chapels 119.0 Books 117.0 Music & Video 117.0 Mags 117.0 Antiques 112.0 Sporting Goods 110.0 Animal Shelters 110.0 Skin Care 110.0 Stadiums & Arenas 109.0 Pop-Up Restaurants 106.0 Guamanian 106.0 Food Tours 105.0 Tobacco Shops 100.0 (306,)
train_test_set['categories'] = train_test_set['categories'].str.findall(main_categories)
train_test_set['categories'] = train_test_set['categories'].map(lambda x: set(x))
train_test_set['categories'] = train_test_set['categories'].map(lambda x: ['Other'] if not bool(x) else list(x))
train_test_set['categories'] = train_test_set['categories'].map(', '.join)
train_test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | OutdoorSeating | BusinessAcceptsCreditCards | RestaurantsDelivery | RestaurantsReservations | WiFi | Alcohol | categories | city | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.622302 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.846154 | 18 | 0 | 1 | 8 | 4 | 4 | 4 | 13.0 | 4.222222 | 9.0 | 3.798692 | 11.168551 | 3.556721 | 5.00000 | 3.686094 | 3.777956 | 3.857143 | 3.684951 | 3.000000 | 5.000000 | 3.868171 | 2.000000 | 3.542997 | 5.000000 | 3.662669 | 3.749776 | 5.000000 | 3.66752 | 3.000000 | 5.000000 | 3.851784 | 2.000000 | 3.555679 | 5.000000 | 3.678871 | 3.770170 | 3.926822 | 3.676024 | 2.866472 | 5.000000 | 3.867280 | 2.000000 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.333333 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | 3.556721 | 3.79608 | 4.000000 | 3.777956 | 5.000000 | 2.500000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 4.000000 | 3.749776 | 5.000000 | 2.50000 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 4.000000 | 3.770170 | 5.000000 | 2.353400 | 3.788204 | 3.928912 | 3.867280 | 3.767263 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 2.571429 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.00000 | 3.000000 | 3.777956 | 3.200000 | 3.684951 | 3.789846 | 1.000000 | 2.333333 | 3.770015 | 2.500000 | 1.000000 | 3.000000 | 3.749776 | 3.200000 | 3.66752 | 3.771654 | 1.000000 | 3.000000 | 3.744434 | 2.505665 | 1.000000 | 2.990709 | 3.770170 | 3.204491 | 3.676024 | 3.788204 | 1.000000 | 2.974502 | 3.767263 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 1 | 0.988395 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | T2P 0K5 | 51.049673 | -114.079977 | 24 | 4.0 | True | None | False | None | None | Beer&Wine | Mexican | Calgary | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | NaT | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 20:00:00 | 04:00:00 | NaT | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.703313 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | L4B 3P7 | 43.841694 | -79.399755 | 44 | 3.0 | False | True | False | True | No | Beer&Wine | Chinese | Richmond Hill | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 11:00:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 22:30:00 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.000000 | 2.980769 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.00000 | 3.686094 | 2.000000 | 3.040000 | 2.600000 | 3.600000 | 2.500000 | 1.000000 | 3.750000 | 2.923077 | 4.000000 | 3.662669 | 1.666667 | 3.146341 | 2.60000 | 3.750000 | 2.500000 | 1.000000 | 3.666667 | 2.954478 | 4.000000 | 3.678871 | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 |
Now we apply the actual conversion
train_test_set.shape
(712379, 93)
cat_cols = ['OutdoorSeating', 'BusinessAcceptsCreditCards', 'RestaurantsDelivery', 'RestaurantsReservations', 'WiFi',
'Alcohol', 'city']
train_test_set = _pd.get_dummies(train_test_set, columns=cat_cols, prefix=cat_cols)
train_test_set.shape
(712379, 281)
categories = train_test_set['categories'].str.get_dummies(',')
f1 = lambda x: "categories_" + x
categories.rename(columns=f1, inplace=True)
train_test_set[categories.columns] = categories
train_test_set.drop(columns=['categories'], inplace=True)
train_test_set.shape
(712379, 787)
oe = _OrdinalEncoder()
ord_cols = ['Monday_Open', 'Tuesday_Open', 'Wednesday_Open', 'Thursday_Open', 'Friday_Open',
'Saturday_Open', 'Sunday_Open', 'Monday_Close', 'Tuesday_Close', 'Wednesday_Close',
'Thursday_Close','Friday_Close', 'Saturday_Close', 'Sunday_Close', 'postal_code']
train_test_set[ord_cols] = oe.fit_transform(train_test_set[ord_cols].to_numpy())
The resulting dataset
train_test_set.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 712379 entries, 0 to 153992 Columns: 787 entries, review_id to categories_Wraps dtypes: float64(68), int32(1), int64(520), object(3), uint8(195) memory usage: 3.3+ GB
train_set = train_test_set[:_train_len]
test_set = train_test_set[_train_len:]
train_set.head(10)
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Allison Park | city_Amherst | city_Aurora | city_Avon | city_Avondale | city_Beachwood | city_Bellevue | city_Belmont | city_Berea | city_Bethel Park | city_Blue Diamond | city_Boulder City | city_Braddock | city_Brampton | city_Brecksville | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklyn | city_Brossard | city_Brunswick | city_Buckeye | city_Calgary | city_Canonsburg | city_Carefree | city_Carnegie | city_Cave Creek | city_Chagrin Falls | city_Champaign | city_Chandler | city_Chardon | city_Charlotte | city_Chesterland | city_Cleveland | city_Clover | city_Concord | city_Coraopolis | city_Cornelius | city_Cuyahoga Falls | city_Davidson | city_Denver | city_Dollard-des-Ormeaux | city_Dorval | city_East York | city_El Mirage | city_Elyria | city_Etobicoke | city_Euclid | city_Fairlawn | city_Fairview Park | city_Fitchburg | city_Fort Mill | city_Fountain Hills | city_Gastonia | city_Gilbert | city_Glendale | city_Goodyear | city_Harrisburg | city_Henderson | city_Highland Heights | city_Homestead | city_Hudson | city_Huntersville | city_Independence | city_Indian Land | city_Indian Trail | city_Irwin | city_Kannapolis | city_Kent | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Laval | city_Laveen | city_Litchfield Park | city_Longueuil | city_Lorain | city_Lyndhurst | city_Macedonia | city_Madison | city_Maple | city_Markham | city_Matthews | city_Mayfield Heights | city_McKees Rocks | city_McMurray | city_Medina | city_Mentor | city_Mesa | city_Middleburg Heights | city_Middleton | city_Mint Hill | city_Mississauga | city_Monona | city_Monroe | city_Monroeville | city_Montreal | city_Montréal | city_Moon Township | city_Mooresville | city_Mount Holly | city_Murrysville | city_New Kensington | city_Newmarket | city_North Las Vegas | city_North Olmsted | city_North Ridgeville | city_North Royalton | city_North York | city_Northfield | city_Oakmont | city_Oakville | city_Olmsted Falls | city_Orange | city_Orange Village | city_Other | city_Painesville | city_Paradise Valley | city_Parma | city_Peoria | city_Phoenix | city_Pickering | city_Pineville | city_Pittsburgh | city_Pointe-Claire | city_Queen Creek | city_Richmond Hill | city_Rock Hill | city_Rocky River | city_Saint-Laurent | city_Scarborough | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Solon | city_South Euclid | city_South Las Vegas | city_Spring Valley | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sun City | city_Sun Prairie | city_Surprise | city_Tega Cay | city_Tempe | city_Thornhill | city_Tolleson | city_Toronto | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Urbana | city_Valley View | city_Vaughan | city_Verdun | city_Verona | city_Warrensville Heights | city_Waunakee | city_Waxhaw | city_West Mifflin | city_Westlake | city_Westmount | city_Wexford | city_Whitby | city_Willoughby | city_Woodbridge | city_Woodmere | city_York | categories_ Acai Bowls | categories_ Active Life | categories_ Adult Entertainment | categories_ Afghan | categories_ African | categories_ Airports | categories_ American (New) | categories_ American (Traditional) | categories_ Amusement Parks | categories_ Animal Shelters | categories_ Antiques | categories_ Appliances | categories_ Arabian | categories_ Arcades | categories_ Argentine | categories_ Armenian | categories_ Art Galleries | categories_ Arts & Crafts | categories_ Arts & Entertainment | categories_ Asian Fusion | categories_ Automotive | categories_ Bagels | categories_ Bakeries | categories_ Bangladeshi | categories_ Barbeque | categories_ Barbers | categories_ Bars | categories_ Basque | categories_ Beauty & Spas | categories_ Bed & Breakfast | categories_ Beer | categories_ Belgian | categories_ Beverage Store | categories_ Bistros | categories_ Books | categories_ Botanical Gardens | categories_ Bowling | categories_ Brasseries | categories_ Brazilian | categories_ Breakfast & Brunch | categories_ Breweries | categories_ Brewpubs | categories_ British | categories_ Bubble Tea | categories_ Buffets | categories_ Burgers | categories_ Butcher | categories_ Cafes | categories_ Cafeteria | categories_ Cajun/Creole | categories_ Cambodian | categories_ Canadian (New) | categories_ Candy Stores | categories_ Cantonese | categories_ Car Wash | categories_ Caribbean | categories_ Casinos | categories_ Caterers | categories_ Cheese Shops | categories_ Cheesesteaks | categories_ Chicken Shop | categories_ Chicken Wings | categories_ Chinese | categories_ Chocolatiers & Shops | categories_ Cinema | categories_ Cocktail Bars | categories_ Coffee & Tea | categories_ Coffee Roasteries | categories_ Colombian | categories_ Comfort Food | categories_ Community Service/Non-Profit | categories_ Convenience Stores | categories_ Conveyor Belt Sushi | categories_ Cooking Classes | categories_ Country Dance Halls | categories_ Creperies | categories_ Cuban | categories_ Cupcakes | categories_ Custom Cakes | categories_ Dance Clubs | categories_ Day Spas | categories_ Delicatessen | categories_ Delis | categories_ Department Stores | categories_ Desserts | categories_ Dim Sum | categories_ Diners | categories_ Dinner Theater | categories_ Distilleries | categories_ Dive Bars | categories_ Do-It-Yourself Food | categories_ Dominican | categories_ Donairs | categories_ Donuts | categories_ Drugstores | categories_ Eatertainment | categories_ Education | categories_ Egyptian | categories_ Empanadas | categories_ Employment Agencies | categories_ Ethiopian | categories_ Ethnic Food | categories_ Ethnic Grocery | categories_ Event Planning & Services | categories_ Eyelash Service | categories_ Falafel | categories_ Farmers Market | categories_ Fashion | categories_ Fast Food | categories_ Festivals | categories_ Filipino | categories_ Fish & Chips | categories_ Fitness & Instruction | categories_ Florists | categories_ Flowers & Gifts | categories_ Fondue | categories_ Food Court | categories_ Food Delivery Services | categories_ Food Stands | categories_ Food Tours | categories_ Food Trucks | categories_ French | categories_ Fruits & Veggies | categories_ Furniture Stores | categories_ Gas Stations | categories_ Gastropubs | categories_ Gay Bars | categories_ Gelato | categories_ German | categories_ Gift Shops | categories_ Gluten-Free | categories_ Golf | categories_ Greek | categories_ Grocery | categories_ Guamanian | categories_ Hair Removal | categories_ Hair Salons | categories_ Hakka | categories_ Halal | categories_ Hawaiian | categories_ Health & Medical | categories_ Health Markets | categories_ Himalayan/Nepalese | categories_ Home & Garden | categories_ Home Decor | categories_ Home Services | categories_ Hong Kong Style Cafe | categories_ Hookah Bars | categories_ Hostels | categories_ Hot Dogs | categories_ Hot Pot | categories_ Hotels | categories_ Hotels & Travel | categories_ Hungarian | categories_ Ice Cream & Frozen Yogurt | categories_ Imported Food | categories_ Indian | categories_ Indonesian | categories_ International | categories_ International Grocery | categories_ Internet Cafes | categories_ Irish | categories_ Italian | categories_ Izakaya | categories_ Japanese | categories_ Jazz & Blues | categories_ Juice Bars & Smoothies | categories_ Karaoke | categories_ Kebab | categories_ Kids Activities | categories_ Kitchen & Bath | categories_ Kombucha | categories_ Korean | categories_ Kosher | categories_ Landmarks & Historical Buildings | categories_ Laotian | categories_ Latin American | categories_ Lebanese | categories_ Live/Raw Food | categories_ Local Flavor | categories_ Local Services | categories_ Lounges | categories_ Macarons | categories_ Mags | categories_ Malaysian | categories_ Massage | categories_ Meat Shops | categories_ Mediterranean | categories_ Mexican | categories_ Middle Eastern | categories_ Mini Golf | categories_ Modern European | categories_ Mongolian | categories_ Moroccan | categories_ Museums | categories_ Music & Video | categories_ Music Venues | categories_ Musicians | categories_ Nail Salons | categories_ Nail Technicians | categories_ New Mexican Cuisine | categories_ Nightlife | categories_ Noodles | categories_ Nutritionists | categories_ Organic Stores | categories_ Outlet Stores | categories_ Pakistani | categories_ Pan Asian | categories_ Party & Event Planning | categories_ Pasta Shops | categories_ Patisserie/Cake Shop | categories_ Performing Arts | categories_ Persian/Iranian | categories_ Personal Chefs | categories_ Personal Shopping | categories_ Peruvian | categories_ Pets | categories_ Piano Bars | categories_ Pizza | categories_ Playgrounds | categories_ Poke | categories_ Polish | categories_ Pool Halls | categories_ Pop-Up Restaurants | categories_ Portuguese | categories_ Poutineries | categories_ Pretzels | categories_ Professional Services | categories_ Public Markets | categories_ Public Services & Government | categories_ Pubs | categories_ Puerto Rican | categories_ Ramen | categories_ Real Estate | categories_ Resorts | categories_ Russian | categories_ Salad | categories_ Salvadoran | categories_ Sandwiches | categories_ Scandinavian | categories_ Seafood | categories_ Shanghainese | categories_ Shaved Ice | categories_ Shaved Snow | categories_ Shopping | categories_ Singaporean | categories_ Skin Care | categories_ Smokehouse | categories_ Social Clubs | categories_ Soul Food | categories_ Soup | categories_ Southern | categories_ Spanish | categories_ Speakeasies | categories_ Specialty Food | categories_ Sporting Goods | categories_ Sports Bars | categories_ Sri Lankan | categories_ Stadiums & Arenas | categories_ Steakhouses | categories_ Street Vendors | categories_ Sushi Bars | categories_ Swimming Pools | categories_ Syrian | categories_ Szechuan | categories_ Tabletop Games | categories_ Tacos | categories_ Taiwanese | categories_ Tapas Bars | categories_ Tapas/Small Plates | categories_ Tea Rooms | categories_ Teppanyaki | categories_ Tex-Mex | categories_ Thai | categories_ Themed Cafes | categories_ Tiki Bars | categories_ Tobacco Shops | categories_ Tours | categories_ Towing | categories_ Turkish | categories_ Tuscan | categories_ Ukrainian | categories_ Vegan | categories_ Vegetarian | categories_ Venezuelan | categories_ Venues & Event Spaces | categories_ Vietnamese | categories_ Waffles | categories_ Waxing | categories_ Wedding Chapels | categories_ Wedding Planning | categories_ Whiskey Bars | categories_ Wholesale Stores | categories_ Wine & Spirits | categories_ Wine Bars | categories_ Wineries | categories_ Wraps | categories_ Zoos | categories_Acai Bowls | categories_Active Life | categories_Afghan | categories_African | categories_Airports | categories_American (New) | categories_American (Traditional) | categories_Appliances | categories_Arabian | categories_Arcades | categories_Argentine | categories_Armenian | categories_Arts & Entertainment | categories_Asian Fusion | categories_Automotive | categories_Bagels | categories_Bakeries | categories_Bangladeshi | categories_Barbeque | categories_Bars | categories_Basque | categories_Beauty & Spas | categories_Beer | categories_Belgian | categories_Beverage Store | categories_Bistros | categories_Bowling | categories_Brasseries | categories_Brazilian | categories_Breakfast & Brunch | categories_Breweries | categories_Brewpubs | categories_British | categories_Bubble Tea | categories_Buffets | categories_Burgers | categories_Butcher | categories_Cafes | categories_Cafeteria | categories_Cajun/Creole | categories_Cambodian | categories_Canadian (New) | categories_Cantonese | categories_Caribbean | categories_Caterers | categories_Cheesesteaks | categories_Chicken Shop | categories_Chicken Wings | categories_Chinese | categories_Coffee & Tea | categories_Coffee Roasteries | categories_Colombian | categories_Comfort Food | categories_Convenience Stores | categories_Cooking Classes | categories_Creperies | categories_Cuban | categories_Custom Cakes | categories_Day Spas | categories_Delicatessen | categories_Delis | categories_Desserts | categories_Dim Sum | categories_Diners | categories_Dinner Theater | categories_Do-It-Yourself Food | categories_Donuts | categories_Education | categories_Ethiopian | categories_Ethnic Grocery | categories_Event Planning & Services | categories_Falafel | categories_Farmers Market | categories_Fashion | categories_Fast Food | categories_Filipino | categories_Fish & Chips | categories_Fondue | categories_Food Court | categories_Food Delivery Services | categories_Food Stands | categories_Food Tours | categories_Food Trucks | categories_French | categories_Furniture Stores | categories_Gas Stations | categories_Gastropubs | categories_Gelato | categories_German | categories_Gluten-Free | categories_Greek | categories_Grocery | categories_Hair Removal | categories_Hair Salons | categories_Hakka | categories_Halal | categories_Hawaiian | categories_Health & Medical | categories_Himalayan/Nepalese | categories_Home & Garden | categories_Home Decor | categories_Home Services | categories_Hot Dogs | categories_Hot Pot | categories_Hotels | categories_Hotels & Travel | categories_Hungarian | categories_Ice Cream & Frozen Yogurt | categories_Imported Food | categories_Indian | categories_Indonesian | categories_International | categories_International Grocery | categories_Internet Cafes | categories_Irish | categories_Italian | categories_Izakaya | categories_Japanese | categories_Juice Bars & Smoothies | categories_Karaoke | categories_Kebab | categories_Korean | categories_Kosher | categories_Landmarks & Historical Buildings | categories_Laotian | categories_Latin American | categories_Lebanese | categories_Live/Raw Food | categories_Local Flavor | categories_Local Services | categories_Mags | categories_Malaysian | categories_Mediterranean | categories_Mexican | categories_Middle Eastern | categories_Modern European | categories_Mongolian | categories_Moroccan | categories_Music Venues | categories_Nail Salons | categories_New Mexican Cuisine | categories_Nightlife | categories_Noodles | categories_Organic Stores | categories_Other | categories_Outlet Stores | categories_Pakistani | categories_Pan Asian | categories_Party & Event Planning | categories_Patisserie/Cake Shop | categories_Performing Arts | categories_Persian/Iranian | categories_Peruvian | categories_Pets | categories_Pizza | categories_Poke | categories_Polish | categories_Pop-Up Restaurants | categories_Portuguese | categories_Poutineries | categories_Public Markets | categories_Public Services & Government | categories_Ramen | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shaved Snow | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Tacos | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Tex-Mex | categories_Thai | categories_Tobacco Shops | categories_Towing | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wineries | categories_Wraps | |
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| 0 | hhVr1uH7XaRlbIHI8dYvbA | FYhU1fKQ7n11WQ7gcFYOag | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.622302 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.846154 | 18 | 0 | 1 | 8 | 4 | 4 | 4 | 13.0 | 4.222222 | 9.0 | 3.798692 | 11.168551 | 3.556721 | 5.00000 | 3.686094 | 3.777956 | 3.857143 | 3.684951 | 3.000000 | 5.000000 | 3.868171 | 2.000000 | 3.542997 | 5.000000 | 3.662669 | 3.749776 | 5.000000 | 3.66752 | 3.000000 | 5.000000 | 3.851784 | 2.000000 | 3.555679 | 5.000000 | 3.678871 | 3.770170 | 3.926822 | 3.676024 | 2.866472 | 5.000000 | 3.867280 | 2.000000 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.333333 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | 3.556721 | 3.79608 | 4.000000 | 3.777956 | 5.000000 | 2.500000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 4.000000 | 3.749776 | 5.000000 | 2.50000 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 4.000000 | 3.770170 | 5.000000 | 2.353400 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 2.571429 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.00000 | 3.000000 | 3.777956 | 3.200000 | 3.684951 | 3.789846 | 1.000000 | 2.333333 | 3.770015 | 2.500000 | 1.000000 | 3.000000 | 3.749776 | 3.200000 | 3.66752 | 3.771654 | 1.000000 | 3.000000 | 3.744434 | 2.505665 | 1.000000 | 2.990709 | 3.770170 | 3.204491 | 3.676024 | 3.788204 | 1.000000 | 2.974502 | 3.767263 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 1 | 0.988395 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.703313 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.000000 | 2.980769 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.00000 | 3.686094 | 2.000000 | 3.040000 | 2.600000 | 3.600000 | 2.500000 | 1.000000 | 3.750000 | 2.923077 | 4.000000 | 3.662669 | 1.666667 | 3.146341 | 2.60000 | 3.750000 | 2.500000 | 1.000000 | 3.666667 | 2.954478 | 4.000000 | 3.678871 | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | rPNt-m2pdt-OR_NMQnHjCQ | tKIihU81IA3NjpsADuR-Tg | --6MefnULPED_I942VcFNA | 5 | 3 | 0 | 2 | 1 | 0.907921 | 4.400000 | 4.400000 | 4.395327 | 4.376569 | 4.405801 | 4.313458 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 4.111111 | 230 | 2 | 12 | 166 | 70 | 32 | 2326 | 9.0 | 4.111111 | 9.0 | 4.132161 | 8.602213 | 4.400000 | 3.79608 | 3.686094 | 3.777956 | 4.500000 | 3.684951 | 3.789846 | 1.000000 | 3.868171 | 5.000000 | 4.400000 | 3.763461 | 3.662669 | 3.749776 | 4.500000 | 3.66752 | 3.771654 | 1.000000 | 3.851784 | 5.000000 | 4.395327 | 3.789966 | 3.678871 | 3.770170 | 4.481197 | 3.676024 | 3.788204 | 1.000000 | 3.867280 | 5.000000 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | Kg582pH05mZO_E6WS8PrKA | XNOs3Wz1Q_zdRgm1Hy05fg | --6MefnULPED_I942VcFNA | 1 | 2 | 2 | 1 | -1 | 0.184327 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 3.703313 | 1 | 0 | 0 | 2 | 1 | 2 | 1462 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | gkW6_UqV9b2XI_5ae8rBCg | HSHuSCJvIvf_Tof62uZPEw | --6MefnULPED_I942VcFNA | 2 | 2 | 1 | 0 | -1 | 0.687126 | 1.800000 | 1.692308 | 1.768813 | 1.816353 | 1.733574 | 1.724494 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 2.543478 | 87 | 1 | 5 | 96 | 34 | 65 | 2158 | 46.0 | 2.581395 | 43.0 | 2.580489 | 38.588153 | 1.800000 | 3.00000 | 3.686094 | 3.166667 | 2.875000 | 2.875000 | 3.000000 | 3.933014 | 1.000000 | 2.000000 | 1.692308 | 3.000000 | 3.662669 | 3.166667 | 3.142857 | 2.87500 | 3.000000 | 3.904608 | 1.000000 | 2.000000 | 1.768813 | 3.166279 | 3.678871 | 3.153689 | 3.005042 | 2.823432 | 2.991269 | 3.928912 | 1.000000 | 2.006375 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 02voOwsYf0cEdKNzt5IkwA | yvpX68yurPsope6KhBZrYA | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 1 | 0.935335 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 23 | 0 | 0 | 23 | 8 | 2 | 286 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | M67I-I5ATaqtVLtKZTgygw | gvh8bvei5vwfoIYbNIvNDQ | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | -1 | 0.242671 | 3.000000 | 3.000000 | 3.040677 | 3.007972 | 3.009842 | 3.062459 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 2.600000 | 21 | 0 | 0 | 6 | 0 | 3 | 4 | 5.0 | 2.666667 | 3.0 | 2.680455 | 3.015285 | 3.556721 | 2.00000 | 3.686094 | 3.777956 | 3.000000 | 2.500000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 2.000000 | 3.662669 | 3.749776 | 3.000000 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 2.000000 | 3.678871 | 3.770170 | 3.040677 | 2.562281 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
train_set.shape
(558386, 787)
train_set.to_pickle('../dataset/m2_n9/model_train_set_3.pickle')
test_set.head(10)
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | 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| categories_Public Markets | categories_Public Services & Government | categories_Ramen | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shaved Snow | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Tacos | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Tex-Mex | categories_Thai | categories_Tobacco Shops | categories_Towing | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wineries | categories_Wraps | |
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| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | -1 | 0.553523 | 1.800000 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 2.875000 | 86 | 0 | 1 | 34 | 9 | 12 | 166 | 16.0 | 3.000000 | 15.0 | 2.909282 | 13.563981 | 1.800000 | 2.20000 | 3.686094 | 4.500000 | 3.333333 | 1.000000 | 3.000000 | 3.933014 | 3.868171 | 3.770015 | 2.000000 | 2.500000 | 3.662669 | 4.500000 | 3.333333 | 1.00000 | 3.000000 | 3.904608 | 3.851784 | 3.744434 | 1.799679 | 2.460018 | 3.678871 | 4.571695 | 3.355656 | 1.000000 | 2.924220 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 1 | 0.990602 | 4.300000 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 4.108108 | 227 | 2 | 7 | 99 | 47 | 30 | 286 | 37.0 | 4.161290 | 31.0 | 4.130733 | 31.326167 | 4.300000 | 3.75000 | 3.686094 | 3.500000 | 4.454545 | 3.666667 | 3.800000 | 3.933014 | 4.000000 | 4.000000 | 4.333333 | 3.750000 | 3.662669 | 3.000000 | 4.555556 | 3.50000 | 4.000000 | 3.904608 | 4.000000 | 4.000000 | 4.299574 | 3.724926 | 3.678871 | 3.340936 | 4.538601 | 3.626374 | 3.946442 | 3.928912 | 4.00000 | 4.000000 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 1 | 0.968214 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 3.703313 | 1 | 0 | 0 | 0 | 0 | 0 | 2110 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 1 | 0.995667 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 59 | 0 | 0 | 5 | 0 | 1 | 22 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | NYG9z-whhsV99RbDR4KPWQ | 3l-Rmqcw_Cm1mTxlqEmLEQ | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 1 | 0 | 5 | 1 | 0.123990 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 17 | 0 | 0 | 7 | 7 | 1 | 4 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 14tBa-RMPWBP93y6KcOyQQ | 2vjNw6qpyvXAqRhSPzmHtQ | --9e1ONYQuAa-CB_Rrw7Tw | 2 | 1 | 0 | 0 | 1 | 0.530155 | 3.000000 | 3.704594 | 3.000000 | 2.995295 | 3.722907 | 3.009147 | 0 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 2.500000 | 105 | 0 | 0 | 31 | 12 | 19 | 4 | 2.0 | 2.000000 | 1.0 | 2.258306 | 1.292847 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.000000 | 2.000000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 2.00000 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.000000 | 2.000000 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | A-n5xtGMR5Frz2KPJTfRzw | WiY9q-Jz42huWzq90fgAWA | --9e1ONYQuAa-CB_Rrw7Tw | 2 | 0 | 0 | 0 | -1 | 0.474595 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 4 | 0 | 1 | 0 | 0 | 0 | 4 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | DqmGICsMu16YttevGUZCjg | q9q9nVaTYz7tScwZLHNO3A | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 1 | 0 | 1 | 0.900049 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 5 | 0 | 0 | 2 | 0 | 1 | 4 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | pSHJpti6SfYIK3XhHzvrXg | q_sv4HEU4XM88x9z6WG-Tw | --9e1ONYQuAa-CB_Rrw7Tw | 2 | 0 | 0 | 0 | -1 | 0.487348 | 4.000000 | 4.000000 | 4.000000 | 4.001269 | 4.001652 | 4.015008 | 0 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.000000 | 10 | 0 | 0 | 4 | 3 | 0 | 22 | 2.0 | 4.000000 | 1.0 | 3.820433 | 0.773019 | 3.556721 | 3.79608 | 3.686094 | 2.000000 | 4.000000 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 4.000000 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 2.000000 | 4.000000 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
test_set.shape
(153993, 787)
test_set.to_pickle('../dataset/m2_n9/model_test_set_3.pickle')
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | 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categories_Wraps | categories_Zoos | |
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| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 1 | 0.993384 | 3.013889 | 3.014925 | 3.039444 | -0.507850 | -0.525615 | -0.455618 | 1 | 4057.0 | 43.579150 | -79.683305 | 51 | 3.5 | 15.0 | 14.0 | 17.0 | 16.0 | 14.0 | 14.0 | 16.0 | 59.0 | 61.0 | 60.0 | 67.0 | 36.0 | 35.0 | 61.0 | 3.521739 | 46.0 | 3.540541 | 37.0 | 3.495062 | 36.481444 | 0.009869 | 3.157143 | 236 | 5 | 25 | 809 | 331 | 162 | 3238 | 140.0 | 3.152672 | 131.0 | 3.158813 | 126.185462 | 3.833333 | 2.87500 | 4.000000 | 3.250000 | 3.013889 | 3.125000 | 3.333333 | 3.400000 | 2.666667 | 2.666667 | 3.823529 | 2.600000 | 4.000000 | 3.250000 | 3.014925 | 3.066667 | 3.333333 | 3.400000 | 2.400000 | 2.666667 | 3.821347 | 2.726907 | 4.000000 | 3.303025 | 3.039444 | 3.145065 | 2.847918 | 3.387395 | 2.572254 | 2.752899 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 1 | 0.519254 | 3.726715 | 3.704594 | 3.719363 | -0.062602 | -0.064146 | -0.054028 | 0 | 493.0 | 33.608745 | -112.359880 | 190 | 2.5 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 27.0 | 59.0 | 61.0 | 60.0 | 67.0 | 71.0 | 70.0 | 61.0 | 2.821229 | 179.0 | 2.814286 | 140.0 | 2.817668 | 133.926571 | 0.009869 | 3.703313 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 1 | 0.983368 | 1.000000 | 1.000000 | 1.000000 | -3.139748 | -3.132280 | -3.125614 | 1 | 530.0 | 36.109837 | -115.174212 | 4286 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 17.0 | 14.0 | 15.0 | 15.0 | 21.0 | 21.0 | 17.0 | 4.139748 | 3814.0 | 4.132280 | 3417.0 | 4.125614 | 3319.856699 | 0.009869 | 1.000000 | 73 | 0 | 1 | 41 | 17 | 32 | 9382 | 2.0 | 1.000000 | 1.0 | 1.000000 | 1.134894 | 3.556721 | 3.79608 | 3.686094 | 1.000000 | 1.000000 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 1.000000 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 1.000000 | 1.000000 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 1 | 0.866956 | 2.750000 | 2.666667 | 2.748879 | -1.212807 | -1.267752 | -1.191953 | 0 | 530.0 | 36.103061 | -115.173450 | 2844 | 4.0 | 29.0 | 26.0 | 30.0 | 25.0 | 23.0 | 25.0 | 29.0 | 0.0 | 9.0 | 9.0 | 67.0 | 9.0 | 9.0 | 8.0 | 3.962807 | 2608.0 | 3.934419 | 2272.0 | 3.940832 | 2229.696159 | 0.009869 | 3.625000 | 548 | 5 | 10 | 273 | 87 | 46 | 1654 | 8.0 | 3.714286 | 7.0 | 3.621997 | 7.577900 | 3.556721 | 3.79608 | 4.000000 | 4.500000 | 5.000000 | 2.750000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 4.000000 | 4.500000 | 5.000000 | 2.666667 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 4.000000 | 4.507847 | 5.000000 | 2.748879 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
#sub_train_set = train_set[:round(train_set.shape[0]/3)]
sub_train_set = train_set
del train_set
sub_train_set.shape
(558386, 817)
# define classifier
svc_classifier = _LinearSVC(random_state=0, max_iter=50000)
svc_classifier.get_params()
{'C': 1.0,
'class_weight': None,
'dual': True,
'fit_intercept': True,
'intercept_scaling': 1,
'loss': 'squared_hinge',
'max_iter': 50000,
'multi_class': 'ovr',
'penalty': 'l2',
'random_state': 0,
'tol': 0.0001,
'verbose': 0}
# fine tune classifier
# param_grid = {'C':[0.001,0.01,0.1,0.25,0.5,0.75,1,10,100,1000], 'gamma':[3,2,1,0.1,0.001,0.0001]}
param_grid = {'C':[0.001,0.01,0.1,0.25,0.5,0.75,1,10,100,1000]}
# grid = _GridSearchCV(estimator=svc_classifier, param_grid=param_grid, refit=True, verbose=2, cv=3, error_score=_np.nan, n_jobs=1, pre_dispatch=1)
grid = _GridSearchCV(estimator=svc_classifier, param_grid=param_grid, refit=True, verbose=2, cv=3, error_score=_np.nan, n_jobs=-1, pre_dispatch=6)
grid.fit(sub_train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), sub_train_set['likes'])
print("best params:", grid.best_params_, "- best score:", grid.best_score_)
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 1654.8min finished
best params: {'C': 0.001} - best score: 0.7436808945783024
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
print("results:", grid.cv_results_)
results: {'mean_fit_time': array([19900.99567477, 19961.755548 , 19843.87850094, 19883.72761846,
19875.85472822, 19892.886506 , 19819.18020558, 19831.93277272,
19672.39210947, 19633.8438464 ]), 'std_fit_time': array([11.92322164, 24.47909948, 11.8002273 , 2.51810785, 18.78170298,
16.00907826, 5.86167115, 32.23873155, 4.59971461, 24.49313577]), 'mean_score_time': array([ 2.63927579, 5.25461706, 5.47336515, 10.05810801, 5.17248909,
3.84837699, 2.75297205, 2.87099091, 2.33608754, 1.75242146]), 'std_score_time': array([0.28703641, 3.56587212, 1.62402285, 4.61803914, 2.93643879,
1.57086447, 0.04191375, 0.27929337, 0.24098951, 0.2409035 ]), 'param_C': masked_array(data=[0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1, 10, 100, 1000],
mask=[False, False, False, False, False, False, False, False,
False, False],
fill_value='?',
dtype=object), 'params': [{'C': 0.001}, {'C': 0.01}, {'C': 0.1}, {'C': 0.25}, {'C': 0.5}, {'C': 0.75}, {'C': 1}, {'C': 10}, {'C': 100}, {'C': 1000}], 'split0_test_score': array([0.74414656, 0.68984581, 0.71937893, 0.57199269, 0.71992156,
0.70982109, 0.59223124, 0.63179498, 0.63454575, 0.63445441]), 'split1_test_score': array([0.74462735, 0.7388034 , 0.70227478, 0.59544507, 0.49747486,
0.62558025, 0.65421645, 0.68013947, 0.66420958, 0.66396781]), 'split2_test_score': array([0.74226876, 0.72382984, 0.55883048, 0.61401831, 0.47335167,
0.52100705, 0.69141129, 0.52799149, 0.52916273, 0.5987976 ]), 'mean_test_score': array([0.74368089, 0.71749292, 0.66016161, 0.59381861, 0.56358326,
0.61880312, 0.6459528 , 0.61330871, 0.60930611, 0.63240661]), 'std_test_score': array([0.00101764, 0.020483 , 0.07199118, 0.0171954 , 0.11098627,
0.0772319 , 0.04090958, 0.06347462, 0.0579493 , 0.02664495]), 'rank_test_score': array([ 1, 2, 3, 9, 10, 6, 4, 7, 8, 5])}
del sub_train_set
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
best_model = grid.best_estimator_
best_model.set_params(verbose=5)
best_model.get_params()
{'C': 0.001,
'class_weight': None,
'dual': True,
'fit_intercept': True,
'intercept_scaling': 1,
'loss': 'squared_hinge',
'max_iter': 50000,
'multi_class': 'ovr',
'penalty': 'l2',
'random_state': 0,
'tol': 0.0001,
'verbose': 5}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=50000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=0)
print("coef:", best_model.coef_)
print("intercept:", best_model.intercept_)
coef: [[-1.43167078e-01 -2.03906910e-01 3.42779107e-01 -1.04728908e-02 -1.47427540e-01 -2.43472370e-02 -9.26808435e-03 -1.17709493e-02 5.58234365e-02 1.83726109e-02 -7.91658762e-03 7.49998933e-05 -3.29092790e-02 7.08513173e-03 -4.64203658e-05 6.24335229e-01 -4.68312019e-05 1.13214836e-03 1.87475912e-04 -8.54227633e-04 -4.47446153e-04 -7.61677236e-04 1.13923954e-03 -1.91138083e-04 -1.40924026e-04 4.50836058e-04 -7.61383085e-05 5.97650915e-05 2.98315416e-05 -2.74831993e-04 -6.26183842e-02 5.30331089e-05 5.84330552e-03 2.03076934e-04 -3.48781537e-02 -2.18059846e-04 -4.02326827e-02 4.95315660e-02 3.39012708e-04 1.71206420e-02 -8.71542990e-04 -2.61593855e-04 1.30803827e-04 1.18480402e-04 -4.80787054e-06 -5.59617233e-03 1.61222017e-02 1.03926925e-04 4.67182627e-03 5.79030033e-03 3.74660499e-02 1.02789779e-02 4.51314090e-02 1.57159326e-02 2.23464027e-02 5.36922329e-02 2.34749232e-02 2.86587882e-02 1.87929188e-02 3.83956193e-04 -7.22989332e-03 1.95846857e-03 1.82299621e-03 1.13746379e-03 5.45619480e-03 1.58677886e-02 -1.95511703e-02 -1.28162672e-02 -1.96649484e-03 -1.30449955e-03 -8.07076837e-04 1.18903675e-02 -1.11566651e-02 2.26932299e-02 2.51582368e-02 -1.87668524e-02 7.85823969e-03 7.73799384e-03 3.85827228e-03 4.22093591e-02 -1.19005102e-01 -8.59189500e-02 -1.24239801e-01 -7.40342751e-02 -1.19770059e-01 -1.35359519e-01 -1.20415320e-01 -1.05257346e-01 -1.03491187e-01 -1.14153703e-01 -1.08812496e-01 -1.06197655e-01 -8.22293134e-02 -8.11447278e-02 -8.84894517e-02 -7.73003600e-02 -8.49805052e-02 -8.51293957e-02 -8.21027373e-02 -7.69512147e-02 6.43923766e-03 1.85595762e-02 1.23813296e-02 2.25462539e-02 2.14381375e-02 -2.21330989e-02 1.39767176e-02 3.28450365e-02 -5.99616006e-02 4.27423193e-02 1.95000974e-02 -7.25494945e-02 -1.51045288e-02 3.20011680e-02 2.51968573e-02 6.58713427e-02 -9.68960933e-03 -7.67558814e-02 -1.31860158e-02 -3.30225057e-02 -1.29565963e-03 -1.22750226e-03 1.32295606e-02 2.79374411e-02 3.17061198e-03 4.61489025e-03 3.31614833e-01 -6.92978644e-03 1.75307888e-02 -8.55135160e-03 2.19597610e-02 1.50690523e-02 4.00123219e-02 -1.79436834e-02 2.37032470e-02 -1.74807415e-01 2.08777300e-02 5.59448146e-02 -2.29348176e-02 -1.07672262e-01 1.36945435e-03 -7.92229306e-02 2.30166566e-02 -2.07064407e-02 -5.24655802e-02 1.29004963e-02 6.08488450e-03 1.29702210e-02 -1.07647542e-02 1.95574537e-02 -6.24734869e-02 1.80610497e-02 1.38307854e-02 7.02867332e-03 3.03232666e-02 -1.18367338e-01 6.66244523e-03 -1.01049018e-01 -2.26197994e-02 2.09783558e-03 -9.12480404e-03 -1.16218404e-02 6.37714957e-02 2.12609828e-02 2.93436435e-02 1.84281145e-02 -8.92567984e-02 2.20758890e-02 -3.20071304e-02 -7.40689432e-02 1.19849983e-02 -4.26474567e-02 3.20709228e-02 -1.48892296e-02 5.93652875e-02 8.70463297e-02 1.68511943e-02 1.08955283e-02 1.15257076e-02 -1.13721061e-02 4.61982521e-03 -5.38403883e-03 1.59303884e-02 1.21825364e-01 -1.43542730e-02 -1.70066160e-02 -1.28769520e-01 -1.08798946e-02 -2.89941634e-02 6.84521535e-03 4.20171560e-02 6.75656292e-02 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2.13004642e-02 -1.60669410e-02 -9.51533592e-03 -5.01331052e-02 7.61613960e-03 -1.04469781e-02 -7.31934059e-04 -2.81985342e-04 2.19386377e-03 3.79455409e-03 -1.11748456e-03 -6.00952125e-03 -9.91363845e-03 -5.09548564e-03 -2.44983072e-02 -1.72975672e-02 9.50968844e-03 -3.62841711e-02 -4.55958794e-03 1.37125824e-03 7.18545900e-03 -1.58720685e-03 -1.44475532e-02 -2.23748903e-02 -4.05693727e-02 3.55632400e-02 -1.30620582e-03 5.38951802e-03 -1.91137097e-02 -4.27591050e-02 -2.73569197e-02 1.27168276e-03 -1.85378351e-02 -7.01738243e-04 -2.83759078e-02 8.08597820e-04 -9.58997930e-03 1.23761410e-03 4.34167906e-03 -6.78296315e-03 3.57030486e-02 -9.62271107e-03 -9.67085263e-03 0.00000000e+00 3.24178422e-04 3.26201083e-02 -7.06086075e-02 1.30559437e-02 -9.62224911e-02 1.89523982e-03 7.78398371e-03 4.54877039e-03 5.78081958e-03 7.11326511e-03 4.15156887e-04 -1.91974270e-02 -7.98532087e-03 2.41789074e-02 -4.11431639e-03 1.28425645e-03 -7.57808090e-03 1.82470266e-02 3.62114623e-02 1.91141494e-02 2.35279337e-02 -7.22572723e-04 -1.88539069e-03 -4.12193347e-02 1.60796818e-02 1.12383544e-02 1.21170106e-04 4.12750278e-03 2.16607546e-03 -1.66681897e-02 2.23429981e-03 -7.14857241e-03 -4.52925993e-05 6.66764106e-03 6.24784033e-03 -2.78202903e-02 1.58978262e-02 3.49680312e-03 -6.48520731e-02 -5.65323893e-02 -1.90193092e-03 3.01207267e-02 -1.31315451e-03 9.92434559e-06 -6.51746958e-04 1.95772688e-03 1.06466202e-02 8.25969551e-05 -9.57170886e-03 -2.20494724e-04 9.06388435e-03 2.11596415e-02 2.60474656e-02 2.26263006e-04 4.14867414e-03 9.75519473e-03 1.56634480e-03 3.96228420e-03 4.81861835e-03 -1.34948169e-02 -5.77011371e-03 1.29384089e-03 1.19237926e-02 -1.14615709e-02 -8.75009192e-03 2.14285082e-02 -4.17197412e-02 -2.44163879e-02 -3.26237878e-02 -5.81602061e-03 -8.90697174e-03 3.72281689e-03 -1.25739551e-02 -2.37849114e-02 -1.89637209e-02 -4.30928889e-02 -2.82602474e-02 1.10434587e-02 -1.18333579e-02 7.62912600e-03 3.36174950e-03 -9.91674983e-03 -3.36842520e-03 6.13852406e-03 -1.23463397e-02 2.70532920e-02 1.34658473e-03 2.58183749e-02 -5.77527036e-03 5.68023344e-02 -9.19399529e-03 1.00286985e-03 7.84130126e-03 0.00000000e+00 -3.01124948e-03 7.41680648e-03 -2.78523695e-02 -2.16936666e-03 1.14407793e-03 -4.08355329e-02 1.12714479e-03 1.00583568e-02 -2.67619252e-02 7.75870591e-03 2.04462887e-02 1.39865985e-02 1.81644059e-02 -1.93742093e-02 1.23097657e-02 2.59880695e-02 5.58663393e-04 -3.45414041e-03 2.05564822e-02 -1.15355666e-02 1.41612339e-03 6.09386922e-03 -1.14223166e-03 -6.27691093e-03 1.57887123e-03 1.56785695e-02 -8.30917111e-03 5.98673265e-03 -1.42472019e-02 -4.01584093e-03 8.80469174e-02 6.22115845e-03 2.91506854e-04 -6.94961622e-04 3.03143045e-03 2.98517155e-04 3.92965342e-03 7.57468421e-03 1.36820283e-02 -4.76946092e-03 6.67322670e-03 -2.88096241e-02 -2.81311951e-02 9.81076193e-03 -2.15307441e-04 3.47471791e-04 5.49986819e-03 3.01861022e-03 -2.62180172e-03 -4.43657911e-02 -2.38224758e-02 8.25245727e-03 -1.44065480e-02 -2.50050522e-02 -7.96490470e-03 1.67455640e-02 1.66711673e-02 7.48092760e-04 1.81851874e-04 4.37139413e-02 -5.55381346e-04 -2.66707086e-03 -2.66720264e-02 1.24151910e-02 -5.70328785e-03 -2.67261397e-02 1.00455909e-03 1.49863493e-02 3.71619172e-03 -7.57665783e-03 -1.82723099e-03 -8.07269119e-03 2.34465032e-02 1.33758856e-02 8.01502638e-03 -1.85376857e-03 7.79391675e-03 5.96944686e-03 -1.88244893e-03 -3.19623791e-03 -2.55225452e-02 1.51594674e-03 1.68299266e-03 2.77837254e-03 9.18093058e-03 -7.20618269e-03 2.63524670e-02 -2.19232243e-02 2.98468545e-02 3.63779680e-02 3.28010985e-02 4.33930745e-03 -1.08360486e-02 -4.29211655e-04 -1.37964806e-02 0.00000000e+00 2.40683045e-02 2.07455480e-02]] intercept: [-0.32916385]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Allison Park | city_Amherst | city_Aurora | city_Avon | city_Avondale | city_Beachwood | city_Bellevue | city_Belmont | city_Berea | city_Bethel Park | city_Blue Diamond | city_Boulder City | city_Braddock | city_Brampton | city_Brecksville | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklyn | city_Brossard | city_Brunswick | city_Buckeye | city_Calgary | city_Canonsburg | city_Carefree | city_Carnegie | city_Cave Creek | city_Chagrin Falls | city_Champaign | city_Chandler | city_Chardon | city_Charlotte | city_Chesterland | city_Cleveland | city_Clover | city_Concord | city_Coraopolis | city_Cornelius | city_Cuyahoga Falls | city_Davidson | city_Denver | city_Dollard-des-Ormeaux | city_Dorval | city_East York | city_El Mirage | city_Elyria | city_Etobicoke | city_Euclid | city_Fairlawn | city_Fairview Park | city_Fitchburg | city_Fort Mill | city_Fountain Hills | city_Gastonia | city_Gilbert | city_Glendale | city_Goodyear | city_Harrisburg | city_Henderson | city_Highland Heights | city_Homestead | city_Hudson | city_Huntersville | city_Independence | city_Indian Land | city_Indian Trail | city_Irwin | city_Kannapolis | city_Kent | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Laval | city_Laveen | city_Litchfield Park | city_Longueuil | city_Lorain | city_Lyndhurst | city_Macedonia | city_Madison | city_Maple | city_Markham | city_Matthews | city_Mayfield Heights | city_McKees Rocks | city_McMurray | city_Medina | city_Mentor | city_Mesa | city_Middleburg Heights | city_Middleton | city_Mint Hill | city_Mississauga | city_Monona | city_Monroe | city_Monroeville | city_Montreal | city_Montréal | city_Moon Township | city_Mooresville | city_Mount Holly | city_Murrysville | city_New Kensington | city_Newmarket | city_North Las Vegas | city_North Olmsted | city_North Ridgeville | city_North Royalton | city_North York | city_Northfield | city_Oakmont | city_Oakville | city_Olmsted Falls | city_Orange | city_Orange Village | city_Other | city_Painesville | city_Paradise Valley | city_Parma | city_Peoria | city_Phoenix | city_Pickering | city_Pineville | city_Pittsburgh | city_Pointe-Claire | city_Queen Creek | city_Richmond Hill | city_Rock Hill | city_Rocky River | city_Saint-Laurent | city_Scarborough | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Solon | city_South Euclid | city_South Las Vegas | city_Spring Valley | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sun City | city_Sun Prairie | city_Surprise | city_Tega Cay | city_Tempe | city_Thornhill | city_Tolleson | city_Toronto | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Urbana | city_Valley View | city_Vaughan | city_Verdun | city_Verona | city_Warrensville Heights | city_Waunakee | city_Waxhaw | city_West Mifflin | city_Westlake | city_Westmount | city_Wexford | city_Whitby | city_Willoughby | city_Woodbridge | city_Woodmere | city_York | categories_ Acai Bowls | categories_ Active Life | categories_ Adult Entertainment | categories_ Afghan | categories_ African | categories_ Airports | categories_ American (New) | categories_ American (Traditional) | categories_ Amusement Parks | categories_ Animal Shelters | categories_ Antiques | categories_ Appliances | categories_ Arabian | categories_ Arcades | categories_ Argentine | categories_ Armenian | categories_ Art Galleries | categories_ Arts & Crafts | categories_ Arts & Entertainment | categories_ Asian Fusion | categories_ Automotive | categories_ Bagels | categories_ Bakeries | categories_ Bangladeshi | categories_ Barbeque | categories_ Barbers | categories_ Bars | categories_ Basque | categories_ Beauty & Spas | categories_ Bed & Breakfast | categories_ Beer | categories_ Belgian | categories_ Beverage Store | categories_ Bistros | categories_ Books | categories_ Botanical Gardens | categories_ Bowling | categories_ Brasseries | categories_ Brazilian | categories_ Breakfast & Brunch | categories_ Breweries | categories_ Brewpubs | categories_ British | categories_ Bubble Tea | categories_ Buffets | categories_ Burgers | categories_ Butcher | categories_ Cafes | categories_ Cafeteria | categories_ Cajun/Creole | categories_ Cambodian | categories_ Canadian (New) | categories_ Candy Stores | categories_ Cantonese | categories_ Car Wash | categories_ Caribbean | categories_ Casinos | categories_ Caterers | categories_ Cheese Shops | categories_ Cheesesteaks | categories_ Chicken Shop | categories_ Chicken Wings | categories_ Chinese | categories_ Chocolatiers & Shops | categories_ Cinema | categories_ Cocktail Bars | categories_ Coffee & Tea | categories_ Coffee Roasteries | categories_ Colombian | categories_ Comfort Food | categories_ Community Service/Non-Profit | categories_ Convenience Stores | categories_ Conveyor Belt Sushi | categories_ Cooking Classes | categories_ Country Dance Halls | categories_ Creperies | categories_ Cuban | categories_ Cupcakes | categories_ Custom Cakes | categories_ Dance Clubs | categories_ Day Spas | categories_ Delicatessen | categories_ Delis | categories_ Department Stores | categories_ Desserts | categories_ Dim Sum | categories_ Diners | categories_ Dinner Theater | categories_ Distilleries | categories_ Dive Bars | categories_ Do-It-Yourself Food | categories_ Dominican | categories_ Donairs | categories_ Donuts | categories_ Drugstores | categories_ Eatertainment | categories_ Education | categories_ Egyptian | categories_ Empanadas | categories_ Employment Agencies | categories_ Ethiopian | categories_ Ethnic Food | categories_ Ethnic Grocery | categories_ Event Planning & Services | categories_ Eyelash Service | categories_ Falafel | categories_ Farmers Market | categories_ Fashion | categories_ Fast Food | categories_ Festivals | categories_ Filipino | categories_ Fish & Chips | categories_ Fitness & Instruction | categories_ Florists | categories_ Flowers & Gifts | categories_ Fondue | categories_ Food Court | categories_ Food Delivery Services | categories_ Food Stands | categories_ Food Tours | categories_ Food Trucks | categories_ French | categories_ Fruits & Veggies | categories_ Furniture Stores | categories_ Gas Stations | categories_ Gastropubs | categories_ Gay Bars | categories_ Gelato | categories_ German | categories_ Gift Shops | categories_ Gluten-Free | categories_ Golf | categories_ Greek | categories_ Grocery | categories_ Guamanian | categories_ Hair Removal | categories_ Hair Salons | categories_ Halal | categories_ Hawaiian | categories_ Health & Medical | categories_ Health Markets | categories_ Himalayan/Nepalese | categories_ Home & Garden | categories_ Home Decor | categories_ Home Services | categories_ Hong Kong Style Cafe | categories_ Hookah Bars | categories_ Hostels | categories_ Hot Dogs | categories_ Hot Pot | categories_ Hotels | categories_ Hotels & Travel | categories_ Hungarian | categories_ Ice Cream & Frozen Yogurt | categories_ Imported Food | categories_ Indian | categories_ Indonesian | categories_ International | categories_ International Grocery | categories_ Internet Cafes | categories_ Irish | categories_ Italian | categories_ Izakaya | categories_ Japanese | categories_ Jazz & Blues | categories_ Juice Bars & Smoothies | categories_ Karaoke | categories_ Kebab | categories_ Kids Activities | categories_ Kitchen & Bath | categories_ Korean | categories_ Kosher | categories_ Landmarks & Historical Buildings | categories_ Laotian | categories_ Latin American | categories_ Lebanese | categories_ Live/Raw Food | categories_ Local Flavor | categories_ Local Services | categories_ Lounges | categories_ Macarons | categories_ Mags | categories_ Malaysian | categories_ Massage | categories_ Meat Shops | categories_ Mediterranean | categories_ Mexican | categories_ Middle Eastern | categories_ Mini Golf | categories_ Modern European | categories_ Mongolian | categories_ Moroccan | categories_ Museums | categories_ Music & Video | categories_ Music Venues | categories_ Musicians | categories_ Nail Salons | categories_ Nail Technicians | categories_ New Mexican Cuisine | categories_ Nightlife | categories_ Noodles | categories_ Nutritionists | categories_ Organic Stores | categories_ Outlet Stores | categories_ Pakistani | categories_ Pan Asian | categories_ Party & Event Planning | categories_ Pasta Shops | categories_ Patisserie/Cake Shop | categories_ Performing Arts | categories_ Persian/Iranian | categories_ Personal Chefs | categories_ Personal Shopping | categories_ Peruvian | categories_ Pets | categories_ Piano Bars | categories_ Pizza | categories_ Playgrounds | categories_ Poke | categories_ Polish | categories_ Pool Halls | categories_ Pop-Up Restaurants | categories_ Portuguese | categories_ Poutineries | categories_ Pretzels | categories_ Professional Services | categories_ Public Markets | categories_ Public Services & Government | categories_ Pubs | categories_ Puerto Rican | categories_ Ramen | categories_ Real Estate | categories_ Resorts | categories_ Russian | categories_ Salad | categories_ Salvadoran | categories_ Sandwiches | categories_ Scandinavian | categories_ Seafood | categories_ Shanghainese | categories_ Shaved Ice | categories_ Shaved Snow | categories_ Shopping | categories_ Singaporean | categories_ Skin Care | categories_ Smokehouse | categories_ Social Clubs | categories_ Soul Food | categories_ Soup | categories_ South African | categories_ Southern | categories_ Spanish | categories_ Speakeasies | categories_ Specialty Food | categories_ Sporting Goods | categories_ Sports Bars | categories_ Sri Lankan | categories_ Stadiums & Arenas | categories_ Steakhouses | categories_ Street Vendors | categories_ Sushi Bars | categories_ Swimming Pools | categories_ Syrian | categories_ Szechuan | categories_ Tabletop Games | categories_ Tacos | categories_ Taiwanese | categories_ Tapas Bars | categories_ Tapas/Small Plates | categories_ Tea Rooms | categories_ Teppanyaki | categories_ Tex-Mex | categories_ Thai | categories_ Themed Cafes | categories_ Tiki Bars | categories_ Tobacco Shops | categories_ Tours | categories_ Towing | categories_ Turkish | categories_ Tuscan | categories_ Ukrainian | categories_ Vegan | categories_ Vegetarian | categories_ Venezuelan | categories_ Venues & Event Spaces | categories_ Vietnamese | categories_ Waffles | categories_ Waxing | categories_ Wedding Planning | categories_ Whiskey Bars | categories_ Wholesale Stores | categories_ Wine & Spirits | categories_ Wine Bars | categories_ Wineries | categories_ Wraps | categories_ Zoos | categories_Acai Bowls | categories_Active Life | categories_Adult Entertainment | categories_Afghan | categories_African | categories_Airports | categories_American (New) | categories_American (Traditional) | categories_Amusement Parks | categories_Arabian | categories_Arcades | categories_Argentine | categories_Armenian | categories_Art Galleries | categories_Arts & Crafts | categories_Arts & Entertainment | categories_Asian Fusion | categories_Automotive | categories_Bagels | categories_Bakeries | categories_Bangladeshi | categories_Barbeque | categories_Bars | categories_Beauty & Spas | categories_Belgian | categories_Bistros | categories_Books | categories_Bowling | categories_Brasseries | categories_Brazilian | categories_Breakfast & Brunch | categories_Breweries | categories_British | categories_Bubble Tea | categories_Buffets | categories_Burgers | categories_Butcher | categories_Cafes | categories_Cafeteria | categories_Cajun/Creole | categories_Cambodian | categories_Canadian (New) | categories_Candy Stores | categories_Cantonese | categories_Car Wash | categories_Caribbean | categories_Casinos | categories_Caterers | categories_Cheese Shops | categories_Cheesesteaks | categories_Chicken Shop | categories_Chicken Wings | categories_Chinese | categories_Cinema | categories_Cocktail Bars | categories_Coffee & Tea | categories_Coffee Roasteries | categories_Comfort Food | categories_Community Service/Non-Profit | categories_Convenience Stores | categories_Conveyor Belt Sushi | categories_Cooking Classes | categories_Creperies | categories_Cuban | categories_Cupcakes | categories_Custom Cakes | categories_Dance Clubs | categories_Day Spas | categories_Delicatessen | categories_Delis | categories_Department Stores | categories_Desserts | categories_Dim Sum | categories_Diners | categories_Dinner Theater | categories_Do-It-Yourself Food | categories_Dominican | categories_Donairs | categories_Donuts | categories_Drugstores | categories_Education | categories_Egyptian | categories_Ethiopian | categories_Ethnic Food | categories_Ethnic Grocery | categories_Event Planning & Services | categories_Farmers Market | categories_Fashion | categories_Fast Food | categories_Festivals | categories_Filipino | categories_Fish & Chips | categories_Fitness & Instruction | categories_Florists | categories_Flowers & Gifts | categories_Fondue | categories_Food Court | categories_Food Delivery Services | categories_Food Stands | categories_Food Trucks | categories_French | categories_Fruits & Veggies | categories_Furniture Stores | categories_Gas Stations | categories_Gastropubs | categories_German | categories_Gift Shops | categories_Gluten-Free | categories_Golf | categories_Greek | categories_Grocery | categories_Hair Removal | categories_Hair Salons | categories_Hakka | categories_Halal | categories_Hawaiian | categories_Health & Medical | categories_Health Markets | categories_Himalayan/Nepalese | categories_Home & Garden | categories_Home Services | categories_Hong Kong Style Cafe | categories_Hot Dogs | categories_Hot Pot | categories_Hotels | categories_Hotels & Travel | categories_Hungarian | categories_Ice Cream & Frozen Yogurt | categories_Imported Food | categories_Indian | categories_Indonesian | categories_International | categories_International Grocery | categories_Internet Cafes | categories_Irish | categories_Italian | categories_Izakaya | categories_Japanese | categories_Jazz & Blues | categories_Juice Bars & Smoothies | categories_Karaoke | categories_Kebab | categories_Kids Activities | categories_Kitchen & Bath | categories_Kombucha | categories_Korean | categories_Kosher | categories_Landmarks & Historical Buildings | categories_Latin American | categories_Live/Raw Food | categories_Local Flavor | categories_Lounges | categories_Macarons | categories_Malaysian | categories_Meat Shops | categories_Mediterranean | categories_Mexican | categories_Middle Eastern | categories_Modern European | categories_Mongolian | categories_Moroccan | categories_Music Venues | categories_New Mexican Cuisine | categories_Nightlife | categories_Noodles | categories_Nutritionists | categories_Other | categories_Outlet Stores | categories_Pakistani | categories_Pan Asian | categories_Party & Event Planning | categories_Pasta Shops | categories_Patisserie/Cake Shop | categories_Performing Arts | categories_Persian/Iranian | categories_Personal Chefs | categories_Personal Shopping | categories_Peruvian | categories_Pets | categories_Pizza | categories_Poke | categories_Polish | categories_Pool Halls | categories_Portuguese | categories_Poutineries | categories_Pretzels | categories_Professional Services | categories_Public Markets | categories_Public Services & Government | categories_Pubs | categories_Puerto Rican | categories_Real Estate | categories_Resorts | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Scandinavian | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Teppanyaki | categories_Tex-Mex | categories_Thai | categories_Tiki Bars | categories_Tobacco Shops | categories_Tours | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Chapels | categories_Wholesale Stores | categories_Wine & Spirits | categories_Wineries | categories_Wraps | categories_Zoos | |
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| 0 | ---j05qHS2X7FkXjjMKKtA | E6Aoz-3s4avfweIjziHjbA | cTbFJzHQzFSX-z3JF4abKQ | 5 | 1 | 0 | 1 | -1 | 0.229833 | 4.000000 | 4.285714 | 3.969038 | 0.674847 | 1.042857 | 0.678155 | 1 | 6145.0 | 43.670859 | -79.393423 | 178 | 3.5 | 58.0 | 55.0 | 59.0 | 61.0 | 57.0 | 58.0 | 61.0 | 68.0 | 69.0 | 69.0 | 76.0 | 76.0 | 75.0 | 70.0 | 3.325153 | 163.0 | 3.242857 | 140.0 | 3.290882 | 138.108392 | 0.009869 | 3.826087 | 465 | 2 | 15 | 1943 | 1617 | 1452 | 7294 | 23.0 | 3.904762 | 21.0 | 3.816160 | 20.600898 | 3.556721 | 4.00000 | 3.686094 | 4.500000 | 3.444444 | 4.000000 | 3.789846 | 3.933014 | 3.868171 | 4.000000 | 3.542997 | 4.000000 | 3.662669 | 4.500000 | 3.444444 | 4.285714 | 3.771654 | 3.904608 | 3.851784 | 4.000000 | 3.555679 | 4.000000 | 3.678871 | 4.508384 | 3.446572 | 3.969038 | 3.788204 | 3.928912 | 3.867280 | 4.102936 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | --4GjusuUCMh24c_oh_cEg | YOOsYiXGEtGFX_wSeZNcww | JytR7WvKyytDQNwOHUzSEg | 4 | 1 | 0 | 0 | 1 | 0.987487 | 3.604167 | 3.607143 | 3.610109 | -0.099146 | -0.096170 | -0.093204 | 1 | 6429.0 | 43.650017 | -79.389188 | 6 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 29.0 | 61.0 | 55.0 | 57.0 | 56.0 | 62.0 | 62.0 | 61.0 | 70.0 | 3.703313 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 0.009869 | 3.610738 | 297 | 5 | 21 | 390 | 179 | 98 | 838 | 149.0 | 3.627907 | 129.0 | 3.617911 | 129.802281 | 3.750000 | 3.75000 | 4.000000 | 3.625000 | 3.604167 | 3.230769 | 2.000000 | 4.400000 | 3.666667 | 3.000000 | 3.750000 | 3.750000 | 4.666667 | 3.625000 | 3.607143 | 3.333333 | 2.000000 | 4.250000 | 3.666667 | 3.000000 | 3.756843 | 3.726028 | 4.287215 | 3.525072 | 3.610109 | 3.170261 | 2.000000 | 4.355007 | 3.664197 | 3.021022 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | --4RpVT5wHJ9AfnZkIC3tw | Bdw4E8jFVd6-CbhrNAJ_EA | -CfFjcCcGGDM9MVH_d42RQ | 5 | 0 | 0 | 0 | -1 | 0.524751 | 5.000000 | 3.704594 | 5.000000 | 1.156489 | -0.064146 | 1.182474 | 1 | 460.0 | 33.276651 | -111.874092 | 369 | 4.0 | 0.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 0.0 | 61.0 | 60.0 | 67.0 | 0.0 | 0.0 | 61.0 | 3.843511 | 262.0 | 3.738636 | 176.0 | 3.817526 | 190.319758 | 0.009869 | 3.666667 | 10 | 0 | 1 | 5 | 2 | 0 | 70 | 3.0 | 3.000000 | 2.0 | 3.403937 | 2.417682 | 3.556721 | 3.79608 | 1.000000 | 3.777956 | 5.000000 | 5.000000 | 3.789846 | 5.000000 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 1.000000 | 3.749776 | 5.000000 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 1.000000 | 3.770170 | 5.000000 | 5.000000 | 3.788204 | 5.000000 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | --4vJzoC0m5h-yodXv-qCw | jm4a1GghQ4zLCN3lQGMQUQ | XMPBg6r_LqZhy9Cf-4ZJrA | 2 | 0 | 0 | 0 | 1 | 0.979875 | 3.726715 | 3.704594 | 3.719363 | -0.062602 | -0.064146 | -0.054028 | 0 | 246.0 | 41.464592 | -81.841481 | 23 | 2.5 | 13.0 | 12.0 | 15.0 | 14.0 | 12.0 | 12.0 | 17.0 | 55.0 | 57.0 | 56.0 | 62.0 | 62.0 | 61.0 | 53.0 | 2.400000 | 20.0 | 2.235294 | 17.0 | 2.369097 | 17.460006 | 0.009869 | 3.703313 | 2 | 0 | 0 | 1 | 0 | 0 | 4 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | --B9JxEb5gY5gAgD2BRhDQ | x_6VdQU3CIdakwHod-dNzA | Fn_IxcCtZl1EoS81sq_s9w | 3 | 1 | 0 | 0 | 1 | 0.662471 | 3.726715 | 3.704594 | 3.719363 | -0.062602 | -0.064146 | -0.054028 | 0 | 438.0 | 33.219015 | -111.791210 | 142 | 3.5 | 0.0 | 37.0 | 41.0 | 40.0 | 27.0 | 25.0 | 29.0 | 0.0 | 57.0 | 56.0 | 62.0 | 67.0 | 66.0 | 54.0 | 3.567376 | 141.0 | 3.575472 | 106.0 | 3.562613 | 111.700460 | 0.009869 | 3.703313 | 3 | 0 | 0 | 1 | 0 | 0 | 574 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
predictions: [1 1 1 ... 1 1 1]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Support Vector Machine:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Support Vector Machine:", _accuracy_score(test_set['likes'], predic)*100)
Report for Support Vector Machine:
precision recall f1-score support
0 0.70 0.36 0.48 50930
1 0.74 0.92 0.82 103063
accuracy 0.74 153993
macro avg 0.72 0.64 0.65 153993
weighted avg 0.73 0.74 0.71 153993
Accuracy for Support Vector Machine: 73.72737721844499
# Confusion matrix for SVC
print("Confusion Matrix for SVC: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for SVC before balance the data:
array([[18347, 32583],
[ 7875, 95188]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("SVM ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_jl.dump(best_model, "../models/best_SVM.joblib")
['../models/best_SVM.joblib']
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Allison Park | city_Amherst | city_Aurora | city_Avon | city_Avondale | city_Beachwood | city_Bellevue | city_Belmont | city_Berea | city_Bethel Park | city_Blue Diamond | city_Boulder City | city_Braddock | city_Brampton | city_Brecksville | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklyn | city_Brossard | city_Brunswick | city_Buckeye | city_Calgary | city_Canonsburg | city_Carefree | city_Carnegie | city_Cave Creek | city_Chagrin Falls | city_Champaign | city_Chandler | city_Chardon | city_Charlotte | city_Chesterland | city_Cleveland | city_Clover | city_Concord | city_Coraopolis | city_Cornelius | city_Cuyahoga Falls | city_Davidson | city_Denver | city_Dollard-des-Ormeaux | city_Dorval | city_East York | city_El Mirage | city_Elyria | city_Etobicoke | city_Euclid | city_Fairlawn | city_Fairview Park | city_Fitchburg | city_Fort Mill | city_Fountain Hills | city_Gastonia | city_Gilbert | city_Glendale | city_Goodyear | city_Harrisburg | city_Henderson | city_Highland Heights | city_Homestead | city_Hudson | city_Huntersville | city_Independence | city_Indian Land | city_Indian Trail | city_Irwin | city_Kannapolis | city_Kent | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Laval | city_Laveen | city_Litchfield Park | city_Longueuil | city_Lorain | city_Lyndhurst | city_Macedonia | city_Madison | city_Maple | city_Markham | city_Matthews | city_Mayfield Heights | city_McKees Rocks | city_McMurray | city_Medina | city_Mentor | city_Mesa | city_Middleburg Heights | city_Middleton | city_Mint Hill | city_Mississauga | city_Monona | city_Monroe | city_Monroeville | city_Montreal | city_Montréal | city_Moon Township | city_Mooresville | city_Mount Holly | city_Murrysville | city_New Kensington | city_Newmarket | city_North Las Vegas | city_North Olmsted | city_North Ridgeville | city_North Royalton | city_North York | city_Northfield | city_Oakmont | city_Oakville | city_Olmsted Falls | city_Orange | city_Orange Village | city_Other | city_Painesville | city_Paradise Valley | city_Parma | city_Peoria | city_Phoenix | city_Pickering | city_Pineville | city_Pittsburgh | city_Pointe-Claire | city_Queen Creek | city_Richmond Hill | city_Rock Hill | city_Rocky River | city_Saint-Laurent | city_Scarborough | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Solon | city_South Euclid | city_South Las Vegas | city_Spring Valley | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sun City | city_Sun Prairie | city_Surprise | city_Tega Cay | city_Tempe | city_Thornhill | city_Tolleson | city_Toronto | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Urbana | city_Valley View | city_Vaughan | city_Verdun | city_Verona | city_Warrensville Heights | city_Waunakee | city_Waxhaw | city_West Mifflin | city_Westlake | city_Westmount | city_Wexford | city_Whitby | city_Willoughby | city_Woodbridge | city_Woodmere | city_York | categories_ Acai Bowls | categories_ Active Life | categories_ Adult Entertainment | categories_ Afghan | categories_ African | categories_ Airports | categories_ American (New) | categories_ American (Traditional) | categories_ Amusement Parks | categories_ Animal Shelters | categories_ Antiques | categories_ Appliances | categories_ Arabian | categories_ Arcades | categories_ Argentine | categories_ Armenian | categories_ Art Galleries | categories_ Arts & Crafts | categories_ Arts & Entertainment | categories_ Asian Fusion | categories_ Automotive | categories_ Bagels | categories_ Bakeries | categories_ Bangladeshi | categories_ Barbeque | categories_ Barbers | categories_ Bars | categories_ Basque | categories_ Beauty & Spas | categories_ Bed & Breakfast | categories_ Beer | categories_ Belgian | categories_ Beverage Store | categories_ Bistros | categories_ Books | categories_ Botanical Gardens | categories_ Bowling | categories_ Brasseries | categories_ Brazilian | categories_ Breakfast & Brunch | categories_ Breweries | categories_ Brewpubs | categories_ British | categories_ Bubble Tea | categories_ Buffets | categories_ Burgers | categories_ Butcher | categories_ Cafes | categories_ Cafeteria | categories_ Cajun/Creole | categories_ Cambodian | categories_ Canadian (New) | categories_ Candy Stores | categories_ Cantonese | categories_ Car Wash | categories_ Caribbean | categories_ Casinos | categories_ Caterers | categories_ Cheese Shops | categories_ Cheesesteaks | categories_ Chicken Shop | categories_ Chicken Wings | categories_ Chinese | categories_ Chocolatiers & Shops | categories_ Cinema | categories_ Cocktail Bars | categories_ Coffee & Tea | categories_ Coffee Roasteries | categories_ Colombian | categories_ Comfort Food | categories_ Community Service/Non-Profit | categories_ Convenience Stores | categories_ Conveyor Belt Sushi | categories_ Cooking Classes | categories_ Country Dance Halls | categories_ Creperies | categories_ Cuban | categories_ Cupcakes | categories_ Custom Cakes | categories_ Dance Clubs | categories_ Day Spas | categories_ Delicatessen | categories_ Delis | categories_ Department Stores | categories_ Desserts | categories_ Dim Sum | categories_ Diners | categories_ Dinner Theater | categories_ Distilleries | categories_ Dive Bars | categories_ Do-It-Yourself Food | categories_ Dominican | categories_ Donairs | categories_ Donuts | categories_ Drugstores | categories_ Eatertainment | categories_ Education | categories_ Egyptian | categories_ Empanadas | categories_ Employment Agencies | categories_ Ethiopian | categories_ Ethnic Food | categories_ Ethnic Grocery | categories_ Event Planning & Services | categories_ Eyelash Service | categories_ Falafel | categories_ Farmers Market | categories_ Fashion | categories_ Fast Food | categories_ Festivals | categories_ Filipino | categories_ Fish & Chips | categories_ Fitness & Instruction | categories_ Florists | categories_ Flowers & Gifts | categories_ Fondue | categories_ Food Court | categories_ Food Delivery Services | categories_ Food Stands | categories_ Food Tours | categories_ Food Trucks | categories_ French | categories_ Fruits & Veggies | categories_ Furniture Stores | categories_ Gas Stations | categories_ Gastropubs | categories_ Gay Bars | categories_ Gelato | categories_ German | categories_ Gift Shops | categories_ Gluten-Free | categories_ Golf | categories_ Greek | categories_ Grocery | categories_ Guamanian | categories_ Hair Removal | categories_ Hair Salons | categories_ Halal | categories_ Hawaiian | categories_ Health & Medical | categories_ Health Markets | categories_ Himalayan/Nepalese | categories_ Home & Garden | categories_ Home Decor | categories_ Home Services | categories_ Hong Kong Style Cafe | categories_ Hookah Bars | categories_ Hostels | categories_ Hot Dogs | categories_ Hot Pot | categories_ Hotels | categories_ Hotels & Travel | categories_ Hungarian | categories_ Ice Cream & Frozen Yogurt | categories_ Imported Food | categories_ Indian | categories_ Indonesian | categories_ International | categories_ International Grocery | categories_ Internet Cafes | categories_ Irish | categories_ Italian | categories_ Izakaya | categories_ Japanese | categories_ Jazz & Blues | categories_ Juice Bars & Smoothies | categories_ Karaoke | categories_ Kebab | categories_ Kids Activities | categories_ Kitchen & Bath | categories_ Korean | categories_ Kosher | categories_ Landmarks & Historical Buildings | categories_ Laotian | categories_ Latin American | categories_ Lebanese | categories_ Live/Raw Food | categories_ Local Flavor | categories_ Local Services | categories_ Lounges | categories_ Macarons | categories_ Mags | categories_ Malaysian | categories_ Massage | categories_ Meat Shops | categories_ Mediterranean | categories_ Mexican | categories_ Middle Eastern | categories_ Mini Golf | categories_ Modern European | categories_ Mongolian | categories_ Moroccan | categories_ Museums | categories_ Music & Video | categories_ Music Venues | categories_ Musicians | categories_ Nail Salons | categories_ Nail Technicians | categories_ New Mexican Cuisine | categories_ Nightlife | categories_ Noodles | categories_ Nutritionists | categories_ Organic Stores | categories_ Outlet Stores | categories_ Pakistani | categories_ Pan Asian | categories_ Party & Event Planning | categories_ Pasta Shops | categories_ Patisserie/Cake Shop | categories_ Performing Arts | categories_ Persian/Iranian | categories_ Personal Chefs | categories_ Personal Shopping | categories_ Peruvian | categories_ Pets | categories_ Piano Bars | categories_ Pizza | categories_ Playgrounds | categories_ Poke | categories_ Polish | categories_ Pool Halls | categories_ Pop-Up Restaurants | categories_ Portuguese | categories_ Poutineries | categories_ Pretzels | categories_ Professional Services | categories_ Public Markets | categories_ Public Services & Government | categories_ Pubs | categories_ Puerto Rican | categories_ Ramen | categories_ Real Estate | categories_ Resorts | categories_ Russian | categories_ Salad | categories_ Salvadoran | categories_ Sandwiches | categories_ Scandinavian | categories_ Seafood | categories_ Shanghainese | categories_ Shaved Ice | categories_ Shaved Snow | categories_ Shopping | categories_ Singaporean | categories_ Skin Care | categories_ Smokehouse | categories_ Social Clubs | categories_ Soul Food | categories_ Soup | categories_ South African | categories_ Southern | categories_ Spanish | categories_ Speakeasies | categories_ Specialty Food | categories_ Sporting Goods | categories_ Sports Bars | categories_ Sri Lankan | categories_ Stadiums & Arenas | categories_ Steakhouses | categories_ Street Vendors | categories_ Sushi Bars | categories_ Swimming Pools | categories_ Syrian | categories_ Szechuan | categories_ Tabletop Games | categories_ Tacos | categories_ Taiwanese | categories_ Tapas Bars | categories_ Tapas/Small Plates | categories_ Tea Rooms | categories_ Teppanyaki | categories_ Tex-Mex | categories_ Thai | categories_ Themed Cafes | categories_ Tiki Bars | categories_ Tobacco Shops | categories_ Tours | categories_ Towing | categories_ Turkish | categories_ Tuscan | categories_ Ukrainian | categories_ Vegan | categories_ Vegetarian | categories_ Venezuelan | categories_ Venues & Event Spaces | categories_ Vietnamese | categories_ Waffles | categories_ Waxing | categories_ Wedding Planning | categories_ Whiskey Bars | categories_ Wholesale Stores | categories_ Wine & Spirits | categories_ Wine Bars | categories_ Wineries | categories_ Wraps | categories_ Zoos | categories_Acai Bowls | categories_Active Life | categories_Adult Entertainment | categories_Afghan | categories_African | categories_Airports | categories_American (New) | categories_American (Traditional) | categories_Amusement Parks | categories_Arabian | categories_Arcades | categories_Argentine | categories_Armenian | categories_Art Galleries | categories_Arts & Crafts | categories_Arts & Entertainment | categories_Asian Fusion | categories_Automotive | categories_Bagels | categories_Bakeries | categories_Bangladeshi | categories_Barbeque | categories_Bars | categories_Beauty & Spas | categories_Belgian | categories_Bistros | categories_Books | categories_Bowling | categories_Brasseries | categories_Brazilian | categories_Breakfast & Brunch | categories_Breweries | categories_British | categories_Bubble Tea | categories_Buffets | categories_Burgers | categories_Butcher | categories_Cafes | categories_Cafeteria | categories_Cajun/Creole | categories_Cambodian | categories_Canadian (New) | categories_Candy Stores | categories_Cantonese | categories_Car Wash | categories_Caribbean | categories_Casinos | categories_Caterers | categories_Cheese Shops | categories_Cheesesteaks | categories_Chicken Shop | categories_Chicken Wings | categories_Chinese | categories_Cinema | categories_Cocktail Bars | categories_Coffee & Tea | categories_Coffee Roasteries | categories_Comfort Food | categories_Community Service/Non-Profit | categories_Convenience Stores | categories_Conveyor Belt Sushi | categories_Cooking Classes | categories_Creperies | categories_Cuban | categories_Cupcakes | categories_Custom Cakes | categories_Dance Clubs | categories_Day Spas | categories_Delicatessen | categories_Delis | categories_Department Stores | categories_Desserts | categories_Dim Sum | categories_Diners | categories_Dinner Theater | categories_Do-It-Yourself Food | categories_Dominican | categories_Donairs | categories_Donuts | categories_Drugstores | categories_Education | categories_Egyptian | categories_Ethiopian | categories_Ethnic Food | categories_Ethnic Grocery | categories_Event Planning & Services | categories_Farmers Market | categories_Fashion | categories_Fast Food | categories_Festivals | categories_Filipino | categories_Fish & Chips | categories_Fitness & Instruction | categories_Florists | categories_Flowers & Gifts | categories_Fondue | categories_Food Court | categories_Food Delivery Services | categories_Food Stands | categories_Food Trucks | categories_French | categories_Fruits & Veggies | categories_Furniture Stores | categories_Gas Stations | categories_Gastropubs | categories_German | categories_Gift Shops | categories_Gluten-Free | categories_Golf | categories_Greek | categories_Grocery | categories_Hair Removal | categories_Hair Salons | categories_Hakka | categories_Halal | categories_Hawaiian | categories_Health & Medical | categories_Health Markets | categories_Himalayan/Nepalese | categories_Home & Garden | categories_Home Services | categories_Hong Kong Style Cafe | categories_Hot Dogs | categories_Hot Pot | categories_Hotels | categories_Hotels & Travel | categories_Hungarian | categories_Ice Cream & Frozen Yogurt | categories_Imported Food | categories_Indian | categories_Indonesian | categories_International | categories_International Grocery | categories_Internet Cafes | categories_Irish | categories_Italian | categories_Izakaya | categories_Japanese | categories_Jazz & Blues | categories_Juice Bars & Smoothies | categories_Karaoke | categories_Kebab | categories_Kids Activities | categories_Kitchen & Bath | categories_Kombucha | categories_Korean | categories_Kosher | categories_Landmarks & Historical Buildings | categories_Latin American | categories_Live/Raw Food | categories_Local Flavor | categories_Lounges | categories_Macarons | categories_Malaysian | categories_Meat Shops | categories_Mediterranean | categories_Mexican | categories_Middle Eastern | categories_Modern European | categories_Mongolian | categories_Moroccan | categories_Music Venues | categories_New Mexican Cuisine | categories_Nightlife | categories_Noodles | categories_Nutritionists | categories_Other | categories_Outlet Stores | categories_Pakistani | categories_Pan Asian | categories_Party & Event Planning | categories_Pasta Shops | categories_Patisserie/Cake Shop | categories_Performing Arts | categories_Persian/Iranian | categories_Personal Chefs | categories_Personal Shopping | categories_Peruvian | categories_Pets | categories_Pizza | categories_Poke | categories_Polish | categories_Pool Halls | categories_Portuguese | categories_Poutineries | categories_Pretzels | categories_Professional Services | categories_Public Markets | categories_Public Services & Government | categories_Pubs | categories_Puerto Rican | categories_Real Estate | categories_Resorts | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Scandinavian | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Teppanyaki | categories_Tex-Mex | categories_Thai | categories_Tiki Bars | categories_Tobacco Shops | categories_Tours | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Chapels | categories_Wholesale Stores | categories_Wine & Spirits | categories_Wineries | categories_Wraps | categories_Zoos | |
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| 0 | ---HLAnHbuLi7vd5TL6uYg | zyp8SaRnZ94sWZpLrifS1Q | l6xZVTEtdZAvNpL1JhYGuw | 4 | 0 | 0 | 0 | -1 | 0.595406 | 3.726715 | 3.704594 | 3.719363 | -0.062602 | -0.064146 | -0.054028 | 1 | 2434.0 | 45.569980 | -73.199634 | 4 | 5.0 | 45.0 | 41.0 | 45.0 | 44.0 | 42.0 | 40.0 | 45.0 | 59.0 | 61.0 | 60.0 | 71.0 | 71.0 | 70.0 | 61.0 | 5.000000 | 3.0 | 5.000000 | 3.0 | 5.000000 | 1.746082 | 0.009869 | 3.000000 | 10 | 0 | 0 | 2 | 0 | 0 | 22 | 3.0 | 3.000000 | 3.0 | 3.022921 | 2.723338 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.000000 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.000000 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.022921 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | ---L4b6VR6HoB-q7cfMWIA | 697iJkhX1mkVF9RNhn114Q | XiXu6WHbDoopKpeg7DfKdQ | 5 | 3 | 1 | 2 | 1 | 0.993384 | 3.013889 | 3.014925 | 3.039444 | -0.507850 | -0.525615 | -0.455618 | 1 | 4057.0 | 43.579150 | -79.683305 | 51 | 3.5 | 15.0 | 14.0 | 17.0 | 16.0 | 14.0 | 14.0 | 16.0 | 59.0 | 61.0 | 60.0 | 67.0 | 36.0 | 35.0 | 61.0 | 3.521739 | 46.0 | 3.540541 | 37.0 | 3.495062 | 36.481444 | 0.009869 | 3.157143 | 236 | 5 | 25 | 809 | 331 | 162 | 3238 | 140.0 | 3.152672 | 131.0 | 3.158813 | 126.185462 | 3.833333 | 2.87500 | 4.000000 | 3.250000 | 3.013889 | 3.125000 | 3.333333 | 3.400000 | 2.666667 | 2.666667 | 3.823529 | 2.600000 | 4.000000 | 3.250000 | 3.014925 | 3.066667 | 3.333333 | 3.400000 | 2.400000 | 2.666667 | 3.821347 | 2.726907 | 4.000000 | 3.303025 | 3.039444 | 3.145065 | 2.847918 | 3.387395 | 2.572254 | 2.752899 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | ---sPYSgArT4Sd5v1nDVMQ | iVSuN8PrtKVtLzhNiu23uA | OumGHdbdp7WgyYMhcAdjhw | 1 | 0 | 0 | 0 | 1 | 0.519254 | 3.726715 | 3.704594 | 3.719363 | -0.062602 | -0.064146 | -0.054028 | 0 | 493.0 | 33.608745 | -112.359880 | 190 | 2.5 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 27.0 | 59.0 | 61.0 | 60.0 | 67.0 | 71.0 | 70.0 | 61.0 | 2.821229 | 179.0 | 2.814286 | 140.0 | 2.817668 | 133.926571 | 0.009869 | 3.703313 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | --0SzSMXVUoAXfackNoB4g | v9P7J6hWWtIblnylQ5UBfA | iCQpiavjjPzJ5_3gPD5Ebg | 5 | 0 | 0 | 0 | 1 | 0.983368 | 1.000000 | 1.000000 | 1.000000 | -3.139748 | -3.132280 | -3.125614 | 1 | 530.0 | 36.109837 | -115.174212 | 4286 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 17.0 | 14.0 | 15.0 | 15.0 | 21.0 | 21.0 | 17.0 | 4.139748 | 3814.0 | 4.132280 | 3417.0 | 4.125614 | 3319.856699 | 0.009869 | 1.000000 | 73 | 0 | 1 | 41 | 17 | 32 | 9382 | 2.0 | 1.000000 | 1.0 | 1.000000 | 1.134894 | 3.556721 | 3.79608 | 3.686094 | 1.000000 | 1.000000 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 1.000000 | 3.667520 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 1.000000 | 1.000000 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | --1JMhPk6K9fZo4FOp_yMw | 2xZ1mHP14as5RJ1KOrVU4A | QJatAcxYgK1Zp9BRZMAx7g | 2 | 0 | 0 | 0 | 1 | 0.866956 | 2.750000 | 2.666667 | 2.748879 | -1.212807 | -1.267752 | -1.191953 | 0 | 530.0 | 36.103061 | -115.173450 | 2844 | 4.0 | 29.0 | 26.0 | 30.0 | 25.0 | 23.0 | 25.0 | 29.0 | 0.0 | 9.0 | 9.0 | 67.0 | 9.0 | 9.0 | 8.0 | 3.962807 | 2608.0 | 3.934419 | 2272.0 | 3.940832 | 2229.696159 | 0.009869 | 3.625000 | 548 | 5 | 10 | 273 | 87 | 46 | 1654 | 8.0 | 3.714286 | 7.0 | 3.621997 | 7.577900 | 3.556721 | 3.79608 | 4.000000 | 4.500000 | 5.000000 | 2.750000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 4.000000 | 4.500000 | 5.000000 | 2.666667 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 4.000000 | 4.507847 | 5.000000 | 2.748879 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
sub_train_set = train_set[:round(train_set.shape[0]/2)]
#sub_train_set = train_set
del train_set
sub_train_set.shape
(279193, 817)
random_forest = _RandomForestClassifier(n_jobs = -1, random_state = 0)
random_forest.get_params()
{'bootstrap': True,
'class_weight': None,
'criterion': 'gini',
'max_depth': None,
'max_features': 'auto',
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_impurity_split': None,
'min_samples_leaf': 1,
'min_samples_split': 2,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 'warn',
'n_jobs': -1,
'oob_score': False,
'random_state': 0,
'verbose': 0,
'warm_start': False}
# fine tune classifier
param_grid = {
'bootstrap': [True, False],
'max_depth': [10, 30, 50],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10],
'n_estimators': [200, 500, 1000],
'criterion': ['gini', 'entropy']}
grid = _GridSearchCV(estimator=random_forest, param_grid=param_grid, refit=False, verbose=5, cv=3, error_score=_np.nan, n_jobs=-1, pre_dispatch=6)
grid.fit(sub_train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), sub_train_set['likes'])
Fitting 3 folds for each of 324 candidates, totalling 972 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=-1)]: Done 12 tasks | elapsed: 25.0min [Parallel(n_jobs=-1)]: Done 66 tasks | elapsed: 134.9min [Parallel(n_jobs=-1)]: Done 156 tasks | elapsed: 558.6min [Parallel(n_jobs=-1)]: Done 282 tasks | elapsed: 1123.2min [Parallel(n_jobs=-1)]: Done 444 tasks | elapsed: 2490.3min [Parallel(n_jobs=-1)]: Done 642 tasks | elapsed: 3490.7min [Parallel(n_jobs=-1)]: Done 876 tasks | elapsed: 4913.4min [Parallel(n_jobs=-1)]: Done 972 out of 972 | elapsed: 5701.6min finished
GridSearchCV(cv=3, error_score=nan,
estimator=RandomForestClassifier(bootstrap=True, class_weight=None,
criterion='gini', max_depth=None,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators='warn', n_jobs=-1,
oob_score=False, random_state=0,
verbose=0, warm_start=False),
iid='warn', n_jobs=-1,
param_grid={'bootstrap': [True, False],
'criterion': ['gini', 'entropy'],
'max_depth': [10, 30, 50],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10],
'n_estimators': [200, 500, 1000]},
pre_dispatch=6, refit=False, return_train_score=False,
scoring=None, verbose=5)
print("best params:\n", grid.best_params_)
print("best score:", grid.best_score_)
best params:
{'bootstrap': False, 'criterion': 'entropy', 'max_depth': 50, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 1000}
best score: 0.7453481999906875
with open("../models/random_forest_params.txt", 'w') as f:
f.write(str(grid.best_params_))
print("results:", grid.cv_results_)
results: {'mean_fit_time': array([ 252.87800407, 600.7321078 , 1147.74082383, 311.25228389,
597.79628388, 1124.06511203, 307.2705942 , 662.01461442,
1102.80993398, 303.65359688, 676.63054156, 1121.13627799,
290.6686426 , 672.74625977, 1103.33752354, 299.24372005,
666.57276177, 1089.51547178, 310.32110922, 677.31205535,
1087.89945881, 302.18119884, 681.54606994, 1077.58835777,
343.88937259, 647.70520091, 1116.92719674, 748.18958219,
1670.8926479 , 3277.3076601 , 726.21665557, 1612.23545631,
3128.41236226, 706.48307268, 1692.42808255, 3044.78944604,
710.94016171, 1687.92898154, 3029.70489526, 716.6063451 ,
1661.93725832, 2975.88908982, 699.15533026, 1650.08494091,
2936.43123945, 663.98700976, 1589.69072064, 2841.49037019,
677.26351619, 1600.60654235, 2818.42968504, 689.23385302,
1575.95244972, 2845.27126567, 854.03630026, 1924.91824579,
3589.90236084, 806.29059982, 1901.73787904, 3542.07222215,
755.210814 , 1842.24359202, 3356.92748769, 772.36362791,
1820.98641507, 3347.10009464, 775.59133355, 1795.96330682,
3266.85892995, 748.71085684, 1781.311143 , 3273.03043238,
728.07022198, 1694.15878701, 3058.85896428, 699.67593479,
1671.264992 , 2974.15439741, 733.93203028, 1685.07307522,
2878.58820073, 316.39686712, 704.58548148, 1315.41525666,
266.52052689, 617.80446243, 1116.14761202, 323.40379898,
596.31457845, 1192.99234486, 306.07180023, 631.73588936,
1107.42891963, 297.97278309, 667.9530731 , 1098.42698288,
303.02261813, 669.46902156, 1087.44833231, 303.09010363,
673.5451235 , 1114.85340317, 306.89094249, 687.86916415,
1097.86382119, 297.0615526 , 674.32869554, 1090.86054126,
759.74303031, 1654.02773229, 3203.89591153, 739.06663569,
1612.05327415, 3126.06397398, 717.29151392, 1664.62706677,
2990.3717099 , 713.38628578, 1674.57015268, 3052.98233859,
706.96910882, 1662.88572113, 2994.57327501, 696.03201071,
1641.71012592, 2944.10638984, 676.90547228, 1583.02720237,
2877.37689845, 686.42369564, 1584.088492 , 2868.15209929,
685.93400542, 1559.20022694, 2867.99252415, 829.33399868,
1916.37242405, 2354.95233067, 793.30663912, 1802.85570685,
2807.89743249, 762.03286791, 1283.57182018, 3463.58312575,
731.26084558, 1854.44696649, 3406.09970792, 775.27916543,
1776.74966931, 3378.01250974, 751.70151536, 1781.36533038,
3312.62044843, 715.7409931 , 1679.43034275, 3086.08285077,
707.93253167, 1689.3303593 , 3104.21390899, 733.89479637,
1658.40256365, 3009.73295474, 387.77073312, 891.25081325,
1669.05788708, 303.89761273, 792.31527678, 1507.48082161,
375.99388091, 780.54043563, 1485.7558972 , 384.29701686,
844.19593382, 1433.76853601, 375.6255339 , 863.93716296,
1459.30460747, 373.0131824 , 874.46136022, 1458.84884469,
366.60730712, 847.71785371, 1464.13846231, 386.84738477,
851.90233604, 1431.67779454, 376.84327952, 868.41485691,
1490.45787962, 1037.9270467 , 2405.62106681, 4825.63027302,
1033.44868628, 2339.84804352, 4577.08206526, 1021.59072081,
2459.0623556 , 4526.76158404, 1010.90661224, 2410.19199936,
4414.15627249, 985.28743219, 2401.53081822, 4428.08470591,
977.47797553, 2381.53228196, 4365.35772228, 952.73910848,
2311.57463264, 4176.69107254, 959.27862748, 2286.79719615,
4208.51034379, 948.67896342, 2275.09348027, 4208.80089919,
1235.81870461, 2858.33534185, 5524.50904346, 1157.73344286,
2814.34227665, 5262.66301656, 1120.64758428, 2752.72798594,
5050.97557322, 1114.87002667, 2681.47512833, 5052.0563302 ,
1080.7848107 , 2693.51625339, 4898.46493554, 1077.82605847,
2566.85985637, 4847.68583147, 1029.18784451, 2430.99676498,
4506.637381 , 1028.56274716, 2459.17040197, 4529.50425267,
1022.89356637, 2430.04791927, 4297.7135512 , 366.22898634,
874.93709048, 1706.81329211, 423.63784893, 806.94950231,
1431.94374752, 387.1473972 , 824.15550756, 1517.69511525,
388.56926513, 841.67227936, 1471.32414134, 372.09363929,
845.24180651, 1432.31375853, 381.16140246, 855.78428976,
1459.6618127 , 386.32925193, 868.99546496, 1440.26550515,
388.21920252, 858.20249327, 1467.01532388, 370.12357601,
863.297539 , 1474.60670225, 1016.42951632, 2406.92572657,
4735.78016353, 1007.15231657, 2369.1623466 , 4589.57932417,
996.3950723 , 2456.83763719, 4445.20094522, 1017.31780783,
2411.83959389, 4458.39833736, 999.47517347, 2423.5928425 ,
4455.27136358, 983.52447764, 2379.59146659, 4360.11706217,
971.04184556, 2321.9312737 , 4224.82240629, 949.03002222,
2311.84523551, 4232.07036241, 954.30591869, 2300.70933517,
4256.19653241, 1227.57042034, 2862.16809591, 5481.17687869,
1156.52833215, 2833.5944798 , 5331.72757514, 1125.75044847,
2745.76227482, 5013.83818905, 1138.77612058, 2735.31786243,
5126.30403113, 1114.51132131, 2684.64896623, 4955.564562 ,
1108.3780485 , 2676.21811597, 4920.55986961, 1029.19738499,
2504.03147769, 4686.9357365 , 1041.30135949, 2464.48220364,
4656.1280938 , 1026.20438417, 2470.45756213, 2600.50956917]), 'std_fit_time': array([ 30.30829678, 7.3920137 , 5.36162458, 6.16287781,
4.71603199, 13.29157039, 30.77081708, 24.55500663,
2.9462911 , 8.21775426, 22.41828531, 17.96219934,
14.42042926, 3.55636399, 3.40924981, 28.19064984,
29.65923753, 9.68237659, 40.21162156, 26.81535584,
2.73972701, 40.72857279, 5.32890289, 9.04349199,
72.35007697, 33.76382866, 12.70825532, 1.1973388 ,
26.28799272, 27.57452577, 19.2399905 , 33.57233439,
7.03525377, 6.96787114, 26.17840237, 21.69617215,
5.72652778, 16.09784563, 5.60459622, 38.07846644,
5.13522422, 6.10484057, 28.8840013 , 9.15673828,
3.77868333, 14.54373134, 22.62081667, 42.12137488,
46.36612411, 48.95809511, 3.71756459, 10.25182197,
38.77469533, 9.91279303, 15.02808539, 3.50673732,
21.2591932 , 33.73133942, 22.16604837, 27.53982584,
6.55108126, 15.15851592, 29.02630199, 13.98807251,
15.6170111 , 5.27449468, 4.71669523, 4.94979628,
8.21982903, 28.0828732 , 6.64810862, 10.47285877,
20.08594434, 21.47197092, 42.8724304 , 13.53014429,
26.3550897 , 16.34642299, 23.8984927 , 35.45706558,
9.59644955, 5.7351674 , 13.51277839, 18.27234975,
31.81341328, 5.88774603, 4.85636529, 42.75257169,
18.926562 , 51.05243492, 14.43683902, 20.65098564,
11.84923354, 5.44938135, 21.60465023, 11.25836454,
24.45109409, 25.05591148, 8.4204074 , 46.71867588,
59.42065962, 16.06614626, 7.70801151, 17.29630675,
4.24164931, 29.93605116, 9.75911225, 19.15552767,
59.62827848, 13.51343739, 27.27137587, 6.4782706 ,
3.12380134, 20.32793836, 6.62062667, 24.18534535,
40.09660025, 49.38004415, 15.97957569, 26.38353192,
14.78771054, 22.02305526, 21.94410114, 29.71031024,
11.65456113, 14.25976303, 39.85181605, 10.32180083,
37.968235 , 33.46566647, 17.14615515, 33.27546592,
13.19411005, 17.38057552, 6.11399812, 21.17612706,
28.40688046, 51.34668928, 12.63107768, 62.71129915,
0.36834374, 4.49424576, 4.33024111, 2.13752287,
3.14354585, 2.83718275, 5.25167961, 1.73576136,
1.3772604 , 6.43414514, 5.18661129, 4.81210139,
4.70039194, 3.52572648, 9.0589487 , 3.79769031,
17.04920476, 10.70438956, 15.14760422, 21.8990582 ,
7.90116057, 7.4321548 , 23.49232366, 8.12951472,
11.25133362, 8.04122486, 22.22850683, 4.90010178,
25.5742791 , 12.17891482, 4.51435936, 8.93586555,
15.36015864, 1.89146275, 18.4743522 , 25.47284129,
21.10220944, 26.36448207, 19.62003341, 0.5027232 ,
20.74194503, 6.88672995, 5.34562602, 17.03257041,
33.73822554, 6.77362179, 3.55545009, 27.0746333 ,
18.11377592, 22.28935143, 8.96315801, 12.64294647,
8.56096107, 12.50241136, 11.83272088, 7.44797562,
56.87594953, 29.40598311, 10.79544944, 15.02910549,
10.99092638, 20.06998157, 21.94652134, 3.90372069,
16.31665264, 28.43944401, 2.07436933, 20.84868541,
7.52533085, 20.92857139, 6.96924701, 12.79672038,
17.67013584, 16.1245658 , 15.55423661, 21.97211009,
35.49759124, 30.27700544, 8.69162715, 12.27562495,
22.06539704, 60.08812146, 14.55264842, 40.72960289,
91.0545018 , 11.58906563, 9.32032197, 30.75423376,
10.83326925, 34.30339473, 112.39188405, 30.24681146,
3.66167295, 31.26381741, 20.76093123, 12.31057523,
39.19107482, 32.69729193, 14.48249089, 17.78150583,
26.80245948, 13.89638986, 31.43113123, 6.77775749,
14.62908714, 29.0209967 , 26.23457069, 28.85436514,
23.38462071, 20.3009723 , 9.2764942 , 39.71354787,
18.69865738, 42.06633349, 19.12809447, 18.12269559,
17.00438512, 2.51240561, 37.01248076, 17.14268623,
20.883293 , 1.64621857, 2.43930551, 1.74417196,
51.9362573 , 17.33219577, 38.648948 , 0.62063819,
14.20026948, 3.15050465, 29.67027009, 22.6174495 ,
46.84971878, 4.36123567, 39.84663268, 17.87730277,
12.26934706, 12.82897682, 29.01803997, 22.89018772,
20.59199815, 1.00230024, 4.11090258, 7.74640375,
28.20557553, 14.72147182, 25.71679854, 17.53483287,
24.18787215, 18.48195342, 9.32786236, 3.19651885,
20.79966976, 19.34164785, 5.23871617, 35.98229101,
9.04758183, 17.21375522, 5.52598687, 12.11320323,
3.80078647, 7.92999699, 12.25112275, 8.95235647,
7.91797862, 233.30396124, 10.83876671, 5.30819944,
16.55791144, 58.16967771, 15.74847618, 60.70317638,
9.28805116, 3.96437891, 9.86981749, 26.12548248,
7.69346146, 41.7562853 , 15.55690244, 12.77329849,
7.44098945, 35.39946121, 13.85279007, 266.83686164]), 'mean_score_time': array([ 22.74486287, 27.54935249, 40.42393446, 20.96096595,
24.16673144, 33.61048714, 24.09159581, 21.4885548 ,
27.27940639, 21.48090823, 23.90609399, 40.81389364,
22.29373638, 25.46559215, 30.36516023, 21.57100264,
21.2904187 , 28.73950942, 20.99520882, 26.80766948,
31.47818518, 22.31800421, 25.20029982, 34.49511989,
20.40345565, 27.27109845, 47.96943299, 38.38139757,
75.43567824, 142.1543235 , 36.46684798, 84.20955404,
117.19570931, 39.00838955, 66.25654753, 124.20829352,
33.95057321, 71.15013234, 113.59234134, 38.96616999,
67.43705702, 128.9812034 , 32.80895972, 62.08403794,
104.8068277 , 33.91367467, 55.18680604, 103.22472239,
33.0865519 , 53.52890388, 91.599799 , 34.16134564,
52.56714249, 111.06709154, 43.28462021, 92.08084838,
204.00564702, 43.39299862, 89.72115509, 156.64757824,
38.93026106, 79.38644799, 154.51361744, 44.90229925,
77.89443612, 134.62910597, 44.05755647, 76.60754387,
148.97974277, 39.98810156, 76.71492179, 124.91507316,
36.80760376, 66.35561681, 106.63028669, 39.01138043,
64.5846839 , 104.55516775, 34.45987995, 64.06706834,
98.58512616, 29.66336902, 32.21454899, 48.465108 ,
22.88017066, 28.25613244, 41.74274349, 22.84559528,
30.67034086, 43.45051614, 22.64645966, 24.40409335,
35.75275524, 20.08098539, 22.798721 , 31.6739943 ,
22.74087501, 29.15207044, 38.58485301, 32.19127655,
21.82200146, 29.69894179, 19.80871503, 21.07599092,
30.05299664, 23.85722311, 27.23353243, 47.80221279,
37.50374508, 83.11082554, 131.89508184, 37.60214726,
80.8807865 , 97.29390987, 37.50208131, 64.13488857,
125.63415074, 36.20155899, 64.83169134, 103.95942688,
37.98412506, 66.09464622, 121.3236746 , 34.38740889,
60.95438727, 103.46906996, 36.68692787, 53.5830938 ,
98.62202724, 35.92562858, 56.79749886, 92.94353922,
33.38409058, 54.9029003 , 105.8390673 , 43.9564929 ,
90.96881978, 74.26267052, 35.22018433, 81.99613849,
69.53909079, 30.86548845, 19.91211637, 165.69738777,
34.17664115, 79.06464195, 131.44495098, 34.8707815 ,
48.8439579 , 159.30879815, 31.32160385, 59.16483959,
77.00315078, 28.22920283, 38.79462775, 117.50687774,
30.0519975 , 51.31648493, 71.80637836, 32.39673042,
36.52602466, 71.91708016, 26.34690038, 28.0852557 ,
27.46225333, 20.06535864, 28.58791248, 39.13438503,
25.13347801, 26.39776413, 33.74811896, 22.44765735,
20.03377787, 36.5669144 , 21.17771935, 28.27840439,
32.7019105 , 21.15378459, 23.29040798, 40.95878116,
21.03643068, 26.94464 , 31.16934427, 22.67404548,
24.22856458, 24.4719158 , 19.63318054, 24.31400243,
55.21044834, 36.6776185 , 81.42533151, 173.99586908,
40.21050549, 90.37806646, 150.76397332, 39.94188897,
73.09294057, 138.47282974, 40.28896332, 73.73821322,
131.70758104, 38.85878762, 80.82327445, 140.61676288,
39.93557556, 67.3748912 , 116.77915653, 37.2760191 ,
69.67506965, 117.97761822, 37.90533606, 68.95566368,
120.58597914, 36.66299057, 68.35793169, 111.96735199,
52.30983059, 115.45236993, 249.59377821, 52.13097493,
109.56909823, 223.20033431, 49.86469928, 114.73362462,
175.27345332, 40.55359038, 94.66859619, 207.41952109,
45.88666828, 105.61533038, 179.65640362, 40.31389523,
87.28633006, 147.3414553 , 38.84282859, 76.10887901,
143.36608315, 40.13105273, 77.18898861, 119.65613238,
41.25371949, 75.5912625 , 127.92535814, 23.59692009,
31.74813008, 48.87002476, 26.67868336, 36.05129306,
45.2753044 , 25.54803848, 30.76608968, 38.37342095,
21.76381874, 27.10653933, 34.0479823 , 22.49087803,
26.45361725, 35.12310855, 22.54938698, 27.99050943,
35.33287899, 17.09662922, 19.62021629, 25.16173673,
21.79473607, 26.04038612, 35.8611358 , 18.33332284,
24.99484897, 40.30558395, 39.98743526, 81.91469073,
161.06310924, 40.43723281, 81.71322838, 137.18660172,
39.01636712, 74.91539947, 129.77674564, 34.74910665,
70.54807472, 141.5206809 , 39.8634367 , 73.80270783,
130.4791983 , 36.25807516, 65.96432996, 120.82633654,
36.08719714, 62.1285855 , 123.06800938, 33.53169688,
58.39689056, 111.50492374, 33.91101575, 61.79248222,
122.0011967 , 45.3614041 , 116.23760295, 242.4129866 ,
48.1316669 , 115.09100095, 232.28936831, 46.3434461 ,
129.92767119, 185.7609218 , 42.03429993, 101.12400738,
192.25539096, 46.57715472, 102.40990162, 170.98325523,
36.91997067, 87.0579412 , 145.6981825 , 38.12441715,
75.73321358, 130.70625877, 38.28798119, 66.29643885,
118.93605439, 43.10410603, 74.20729534, 34.68228738]), 'std_score_time': array([ 3.73790697, 0.30415656, 6.92587549, 2.80850105, 1.15761239,
11.03193416, 2.18269738, 5.7843071 , 6.68902138, 4.04390138,
6.90222302, 7.87328813, 4.49783489, 2.72922677, 7.98650193,
1.52263762, 5.5064888 , 3.48344337, 0.47871017, 3.73582713,
4.34613959, 2.36834954, 1.46729828, 5.89990151, 1.61809314,
3.84808417, 5.0427848 , 2.30684875, 4.81018346, 10.27616733,
2.74101451, 3.79907696, 3.98529257, 4.40175377, 2.61252675,
5.53625533, 3.37931338, 1.65719739, 1.86480187, 0.65404252,
5.25289516, 4.36917093, 3.74452769, 4.16126799, 5.23263841,
3.13796786, 7.31170198, 7.82135388, 1.95445351, 4.38168262,
10.20791492, 3.05717731, 3.1417052 , 6.26757503, 4.58113999,
11.0739803 , 6.86371335, 4.36039996, 2.19959567, 1.47891058,
2.45664485, 3.11531965, 1.32099943, 4.53794374, 8.37548576,
9.20223396, 4.9506376 , 1.53459068, 0.28753037, 2.98474871,
3.14313634, 1.19331635, 2.89314709, 0.77832221, 4.2531609 ,
2.10041339, 4.41834301, 5.39228948, 3.26462031, 2.35477828,
3.51190062, 9.50254451, 0.72167435, 1.73167494, 2.07519971,
4.71073193, 2.34675829, 2.15203514, 3.67366729, 6.62659062,
2.37555739, 2.51017608, 7.64574316, 2.75829883, 0.63760264,
4.81559447, 1.73884145, 4.53266327, 5.38363662, 10.35304377,
4.83179403, 7.91551107, 1.98318509, 5.88545707, 2.44557327,
1.27360577, 3.45900966, 5.74310212, 1.51303855, 3.9097375 ,
10.87698257, 4.39227344, 8.45290361, 9.14867945, 5.90746222,
5.87062798, 5.80196265, 2.26114274, 0.93526402, 6.58166393,
0.46877206, 6.95247138, 12.93359514, 1.36948163, 4.16947603,
5.69149097, 1.43797017, 9.73990258, 2.87744378, 3.04764653,
5.66657796, 6.6383046 , 1.88310248, 4.53918743, 14.56719218,
4.5464202 , 3.90785792, 16.59522595, 5.77814509, 13.45240866,
6.98776699, 3.54062851, 0.12516608, 0.24607868, 0.66054981,
5.3054573 , 2.77873505, 1.25965318, 4.11532532, 2.06323342,
1.99657908, 1.66333947, 4.66710504, 0.55396838, 6.17189624,
3.75059384, 3.96108726, 4.51974468, 19.41740517, 3.72227094,
15.4103076 , 12.85118308, 2.91415565, 2.36420569, 7.39524787,
1.42865041, 7.54756348, 5.08740656, 4.49961882, 2.20533559,
12.30644421, 2.95896234, 8.56015294, 9.45873904, 0.76678208,
0.8716595 , 6.08612062, 2.98883532, 6.7323311 , 3.27947057,
0.14285844, 4.45707189, 6.15440368, 1.70068028, 4.83515932,
5.03018124, 2.97086614, 5.4358429 , 2.0888408 , 3.05860615,
7.75705428, 4.24975645, 4.34786636, 4.31404968, 4.11678185,
1.75035116, 3.61430526, 0.45768793, 7.25933701, 2.53974597,
9.3456339 , 2.07941927, 3.03685232, 7.37798915, 4.90112696,
6.08297988, 5.68544687, 3.41365848, 5.19504503, 6.27730193,
4.02094883, 2.10016744, 10.31717127, 2.40784982, 3.76999755,
10.16835344, 3.02120987, 1.6133806 , 17.22251422, 5.18892895,
4.67723033, 13.7662241 , 7.40142595, 13.31781516, 9.85166008,
5.56724033, 7.27027481, 21.16881461, 4.52884724, 20.45550601,
4.03839222, 3.6416739 , 3.15661309, 7.46747975, 3.01372365,
5.64001772, 3.40005478, 3.07717959, 3.0524081 , 5.48947086,
2.04908638, 3.11329794, 3.7344732 , 0.97981083, 2.09441675,
4.67694722, 1.61361817, 5.13391137, 2.10023593, 0.77977137,
1.35841737, 6.64283177, 1.23429067, 2.70003199, 6.35901372,
2.15777896, 6.75359925, 6.36054868, 1.70093369, 3.16029066,
5.18878811, 0.54288971, 0.31328513, 2.14584227, 2.06049559,
6.19079603, 8.08457458, 1.53124759, 3.72749796, 1.61386877,
4.85124001, 2.60318048, 21.41306405, 0.44808795, 1.72836091,
1.57733826, 1.68303042, 7.2501898 , 7.81201401, 3.84858105,
7.92796487, 4.47742288, 2.39859737, 2.52222502, 6.60925813,
3.56953739, 5.61733233, 1.80074324, 2.59539304, 6.54945999,
9.54982783, 5.54282533, 8.48413645, 6.77137514, 2.65786256,
6.28151036, 6.16077712, 3.38522252, 7.16557433, 15.11352743,
0.61040563, 16.13892246, 9.48657747, 9.43038419, 25.17788277,
0.63656661, 2.00818815, 14.88494676, 13.4548406 , 0.60568569,
17.64370399, 5.56989484, 5.03281369, 2.0426668 , 5.30472464,
0.98666712, 5.50817538, 17.00630192, 1.30159059, 11.35127102,
5.20120321, 2.80094782, 5.02332418, 16.61052917]), 'param_bootstrap': masked_array(data=[True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object), 'param_criterion': masked_array(data=['gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',
'gini', 'gini', 'gini', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy', 'entropy',
'entropy', 'entropy', 'entropy', 'entropy'],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object), 'param_max_depth': masked_array(data=[10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30, 30, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object), 'param_min_samples_leaf': masked_array(data=[1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
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0.74277916, 0.74275767, 0.74270395, 0.7442835 , 0.74377848,
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0.74551921, 0.7453258 , 0.74586306, 0.74607797, 0.74643256,
0.7449712 , 0.74572337, 0.74557294, 0.7449712 , 0.74572337,
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0.72653228, 0.72639259, 0.72662899, 0.74183358, 0.74253202,
0.74271469, 0.74209146, 0.7430263 , 0.74255351, 0.74217743,
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0.7430263 , 0.74364953, 0.74387518, 0.74174761, 0.74191954,
0.74208072, 0.74174761, 0.74191954, 0.74208072, 0.74230637,
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0.72675793, 0.72706954, 0.72701582, 0.72708029, 0.7276283 ,
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0.72654302, 0.72655377, 0.72574787, 0.72754234, 0.72738116,
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0.74267171, 0.74262873, 0.74297258, 0.7433809 , 0.74327345,
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0.74214519, 0.74261798, 0.74228488, 0.74219892, 0.742575 ,
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0.74300481, 0.74401487, 0.743714 , 0.74401487, 0.74195178,
0.7429296 , 0.743714 , 0.74195178, 0.7429296 , 0.743714 ,
0.74268246, 0.74343463, 0.74327345, 0.72652153, 0.72679017,
0.72702656, 0.72617768, 0.72666122, 0.72674718, 0.72657526,
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0.72672569, 0.72456589, 0.72653228, 0.72657526, 0.72456589,
0.72653228, 0.72657526, 0.72560818, 0.72596278, 0.72630663,
0.74201625, 0.74261798, 0.74280065, 0.74347761, 0.74378922,
0.74391816, 0.74321972, 0.74374624, 0.74353133, 0.74392891,
0.74392891, 0.74364953, 0.74306929, 0.74397189, 0.74355282,
0.74263947, 0.74343463, 0.74368177, 0.74294034, 0.74270395,
0.74315525, 0.74294034, 0.74270395, 0.74315525, 0.74215594,
0.74266096, 0.74282214, 0.73719161, 0.73719161, 0.73727757,
0.74097395, 0.74206997, 0.74204848, 0.74327345, 0.74345612,
0.74373549, 0.74272544, 0.74309078, 0.74320897, 0.74283289,
0.74341314, 0.74367102, 0.74385369, 0.74425127, 0.74435872,
0.74270395, 0.74377848, 0.74404711, 0.74270395, 0.74377848,
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0.72746451, 0.72721737, 0.72752899, 0.72764718, 0.72777971,
0.72770449, 0.72787642, 0.72771524, 0.7276436 , 0.72769375,
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0.72769375, 0.72770807, 0.74268338, 0.74344629, 0.74343913,
0.74299857, 0.74377939, 0.74406593, 0.74302722, 0.74379372,
0.74380088, 0.74308095, 0.7434248 , 0.74340331, 0.74272278,
0.743346 , 0.74324571, 0.74245056, 0.743346 , 0.74345704,
0.74197061, 0.74235386, 0.7424828 , 0.74197061, 0.74235386,
0.7424828 , 0.74233237, 0.74240042, 0.74251862, 0.73992901,
0.74082803, 0.74103219, 0.74265114, 0.7433066 , 0.74349643,
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0.74400146, 0.74270487, 0.74368627, 0.74399788, 0.74365761,
0.74413757, 0.74406235, 0.74209597, 0.74274427, 0.74286963,
0.74209597, 0.74274427, 0.74286963, 0.74186674, 0.74287321,
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0.7270347 , 0.72695591, 0.72700605, 0.7270347 , 0.72695591,
0.72706694, 0.72680189, 0.72709559, 0.7429341 , 0.74357523,
0.74401221, 0.74332093, 0.74404444, 0.74388326, 0.74365403,
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0.7434642 , 0.74401937, 0.74401579, 0.74262965, 0.74346062,
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0.74299141, 0.74286963, 0.74233953, 0.742601 , 0.74237176,
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0.74305946, 0.74323138, 0.74392266, 0.74423786, 0.74454231,
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0.74475721, 0.7445638 , 0.74373999, 0.74397639, 0.74410175,
0.74373999, 0.74397639, 0.74410175, 0.74358956, 0.7439155 ,
0.74385103, 0.73608221, 0.73677349, 0.73691676, 0.74067043,
0.74112531, 0.74136171, 0.74301648, 0.74394774, 0.74405161,
0.74328153, 0.74392983, 0.7439943 , 0.74352151, 0.74382954,
0.74425218, 0.74445634, 0.74473572, 0.74487183, 0.74348211,
0.7441125 , 0.74463185, 0.74348211, 0.7441125 , 0.74463185,
0.74369701, 0.74420204, 0.74418055, 0.72690576, 0.72698814,
0.7271529 , 0.72650819, 0.72673742, 0.72705619, 0.72718156,
0.72721021, 0.72707768, 0.72685203, 0.72651177, 0.72677682,
0.72697381, 0.72733557, 0.7271529 , 0.72647237, 0.72678756,
0.72714216, 0.72687352, 0.72691651, 0.72675891, 0.72687352,
0.72691651, 0.72675891, 0.72667653, 0.72662638, 0.72661564,
0.74328153, 0.74416264, 0.74441336, 0.74429875, 0.74471423,
0.74459245, 0.74450291, 0.74488257, 0.74480736, 0.74428084,
0.7446999 , 0.74469274, 0.7439943 , 0.74470707, 0.74471781,
0.74410891, 0.74482526, 0.74488257, 0.74405519, 0.74424502,
0.74443844, 0.74405519, 0.74424502, 0.74443844, 0.74342122,
0.74396564, 0.74409817, 0.73784801, 0.73831364, 0.73837453,
0.7416769 , 0.74230729, 0.74225357, 0.74386177, 0.74401579,
0.7444456 , 0.7437579 , 0.74446709, 0.74444202, 0.74372925,
0.74449574, 0.74456021, 0.7447393 , 0.74502584, 0.7453482 ,
0.7438761 , 0.74455305, 0.74467125, 0.7438761 , 0.74455305,
0.74467125, 0.74441336, 0.74473214, 0.74478586]), 'std_test_score': array([0.0011383 , 0.00098686, 0.00082527, 0.00050995, 0.00036684,
0.00049991, 0.0008291 , 0.00101392, 0.00096373, 0.00110867,
0.00081422, 0.00048827, 0.0002882 , 0.00017974, 0.00040267,
0.00049651, 0.00055225, 0.00065357, 0.00104007, 0.00071387,
0.00059789, 0.00104007, 0.00071387, 0.00059789, 0.00038917,
0.00050514, 0.00055694, 0.00128026, 0.00123573, 0.00112777,
0.00111472, 0.00128469, 0.00123451, 0.00109336, 0.00168767,
0.00144937, 0.00098779, 0.00128426, 0.00123816, 0.00142963,
0.00116408, 0.00121513, 0.00103317, 0.00082286, 0.00084226,
0.00040323, 0.0009691 , 0.00091491, 0.00040323, 0.0009691 ,
0.00091491, 0.00122722, 0.00125838, 0.00129848, 0.00059453,
0.00080846, 0.00081596, 0.00073837, 0.0004652 , 0.00061034,
0.00052888, 0.00084352, 0.00092317, 0.00091359, 0.00090329,
0.00072064, 0.00053352, 0.00064877, 0.00079991, 0.00115647,
0.00132175, 0.0010696 , 0.00151581, 0.00120883, 0.00116018,
0.00151581, 0.00120883, 0.00116018, 0.00101962, 0.0014915 ,
0.00141746, 0.00119208, 0.00087439, 0.00071807, 0.00052615,
0.00069859, 0.00068688, 0.00109707, 0.00125211, 0.00089393,
0.00086852, 0.00057916, 0.00067339, 0.00072293, 0.00057856,
0.0006989 , 0.00068957, 0.00047045, 0.00070921, 0.00111668,
0.0005793 , 0.00059732, 0.00111668, 0.0005793 , 0.00059732,
0.00037921, 0.00033908, 0.00035165, 0.00083144, 0.00089801,
0.00098829, 0.00093875, 0.00072324, 0.0010792 , 0.00122431,
0.0013652 , 0.00135997, 0.00075842, 0.00093344, 0.00110297,
0.00112411, 0.00131035, 0.0011806 , 0.00088393, 0.00118963,
0.00099057, 0.00105304, 0.00089091, 0.00109392, 0.00105304,
0.00089091, 0.00109392, 0.0010309 , 0.00140226, 0.00131647,
0.00049069, 0.00070462, 0.00057114, 0.0005172 , 0.00035081,
0.00054183, 0.00027374, 0.00064951, 0.00066253, 0.00050631,
0.00052004, 0.00035349, 0.00045741, 0.00090125, 0.00084723,
0.00077669, 0.00092558, 0.00063609, 0.00100819, 0.00107015,
0.00097762, 0.00100819, 0.00107015, 0.00097762, 0.00102799,
0.0010012 , 0.0011079 , 0.00038483, 0.00047484, 0.00051342,
0.00033745, 0.00065568, 0.00073408, 0.0010333 , 0.00063909,
0.00056192, 0.00088178, 0.00077017, 0.00058499, 0.00141294,
0.00098429, 0.00096509, 0.00131507, 0.00044722, 0.0004692 ,
0.00130658, 0.00083732, 0.00074498, 0.00130658, 0.00083732,
0.00074498, 0.00054093, 0.00079753, 0.00074467, 0.00062117,
0.00049425, 0.00061709, 0.00070502, 0.00065848, 0.00090458,
0.00130889, 0.00108392, 0.00114516, 0.00139435, 0.0009186 ,
0.00090076, 0.00083003, 0.00090823, 0.00104014, 0.00061023,
0.00106035, 0.00104985, 0.00112968, 0.0010466 , 0.00137042,
0.00112968, 0.0010466 , 0.00137042, 0.00109874, 0.00107118,
0.00084665, 0.00063183, 0.00060483, 0.00076879, 0.00064292,
0.00043669, 0.0007811 , 0.00089047, 0.00064495, 0.00054776,
0.0006557 , 0.00073253, 0.0005976 , 0.00069373, 0.00063131,
0.00088408, 0.00050588, 0.00083348, 0.0007712 , 0.00140629,
0.0011616 , 0.00088676, 0.00140629, 0.0011616 , 0.00088676,
0.00088656, 0.00083191, 0.00092125, 0.00083806, 0.00059883,
0.00079782, 0.00106015, 0.00069837, 0.00076753, 0.00044685,
0.00059473, 0.0005281 , 0.00095151, 0.00068708, 0.00073449,
0.00059844, 0.00061246, 0.0007071 , 0.00075882, 0.00059155,
0.00050574, 0.00222771, 0.00132113, 0.00120341, 0.00222771,
0.00132113, 0.00120341, 0.00146346, 0.00089871, 0.00100546,
0.00093417, 0.00109243, 0.00114316, 0.00077421, 0.00072971,
0.0006019 , 0.00105285, 0.00085223, 0.00098095, 0.00026651,
0.00064074, 0.00093439, 0.00066874, 0.00055883, 0.00086314,
0.00125327, 0.00116814, 0.00107622, 0.0009853 , 0.00121742,
0.00106186, 0.0009853 , 0.00121742, 0.00106186, 0.00103089,
0.00114798, 0.0010668 , 0.00047298, 0.00081946, 0.00078515,
0.00078921, 0.00031862, 0.00031889, 0.00042885, 0.00057871,
0.0005267 , 0.00074163, 0.00097379, 0.00088294, 0.00065293,
0.00086082, 0.00068119, 0.00083743, 0.00077114, 0.0008493 ,
0.0009272 , 0.00084187, 0.0006531 , 0.0009272 , 0.00084187,
0.0006531 , 0.0008132 , 0.00061441, 0.00069014]), 'rank_test_score': array([305, 253, 243, 274, 257, 245, 260, 242, 235, 224, 232, 218, 230,
236, 233, 307, 225, 240, 290, 258, 246, 290, 258, 246, 241, 233,
231, 178, 135, 136, 163, 109, 78, 161, 108, 107, 158, 137, 139,
176, 142, 151, 187, 142, 134, 198, 190, 185, 198, 190, 185, 193,
188, 184, 210, 207, 206, 179, 147, 128, 117, 47, 64, 148, 76,
89, 177, 120, 90, 121, 67, 79, 196, 174, 169, 196, 174, 169,
200, 168, 157, 299, 285, 280, 289, 292, 262, 285, 277, 260, 314,
298, 281, 322, 319, 317, 270, 262, 296, 281, 277, 294, 281, 277,
294, 274, 308, 269, 167, 124, 88, 146, 83, 101, 122, 61, 59,
156, 117, 97, 132, 85, 86, 180, 133, 127, 182, 164, 169, 182,
164, 169, 192, 181, 189, 208, 202, 201, 166, 116, 84, 125, 35,
47, 114, 55, 41, 111, 69, 28, 77, 26, 23, 154, 144, 140,
154, 144, 140, 159, 153, 131, 324, 296, 285, 292, 228, 217, 248,
219, 221, 222, 227, 248, 244, 223, 229, 252, 225, 237, 272, 255,
248, 272, 255, 248, 238, 239, 220, 173, 160, 152, 99, 58, 33,
65, 8, 3, 66, 51, 34, 67, 17, 36, 37, 11, 29, 112,
93, 73, 112, 93, 73, 123, 100, 105, 216, 215, 214, 209, 205,
204, 162, 96, 82, 148, 98, 91, 126, 106, 54, 41, 13, 6,
129, 70, 24, 129, 70, 24, 119, 60, 62, 302, 284, 266, 321,
313, 276, 265, 262, 270, 306, 320, 310, 285, 254, 266, 322, 309,
268, 303, 300, 311, 303, 300, 311, 315, 316, 318, 148, 63, 49,
52, 16, 27, 38, 4, 9, 53, 19, 20, 91, 18, 15, 72,
7, 4, 80, 55, 45, 80, 55, 45, 138, 95, 75, 213, 212,
211, 203, 194, 195, 104, 86, 43, 110, 40, 44, 115, 39, 30,
12, 2, 1, 102, 31, 21, 102, 31, 21, 49, 14, 10])}
del sub_train_set
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
params = grid.best_params_
params['n_jobs'] = -1
params['verbose'] = 5
best_model = _RandomForestClassifier(**params)
best_model.get_params()
{'bootstrap': False,
'class_weight': None,
'criterion': 'entropy',
'max_depth': 50,
'max_features': 'auto',
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_impurity_split': None,
'min_samples_leaf': 2,
'min_samples_split': 10,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 1000,
'n_jobs': -1,
'oob_score': False,
'random_state': None,
'verbose': 5,
'warm_start': False}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.
building tree 1 of 1000 building tree 2 of 1000 building tree 3 of 1000 building tree 4 of 1000 building tree 5 of 1000 building tree 6 of 1000 building tree 7 of 1000 building tree 8 of 1000 building tree 9 of 1000 building tree 10 of 1000 building tree 11 of 1000 building tree 12 of 1000 building tree 13 of 1000 building tree 14 of 1000 building tree 15 of 1000 building tree 16 of 1000 building tree 17 of 1000 building tree 18 of 1000 building tree 19 of 1000 building tree 20 of 1000 building tree 21 of 1000 building tree 22 of 1000 building tree 23 of 1000 building tree 24 of 1000 building tree 25 of 1000 building tree 26 of 1000 building tree 27 of 1000 building tree 28 of 1000 building tree 29 of 1000 building tree 30 of 1000 building tree 31 of 1000 building tree 32 of 1000 building tree 33 of 1000 building tree 34 of 1000 building tree 35 of 1000 building tree 36 of 1000 building tree 37 of 1000 building tree 38 of 1000 building tree 39 of 1000 building tree 40 of 1000 building tree 41 of 1000 building tree 42 of 1000 building tree 43 of 1000 building tree 44 of 1000 building tree 45 of 1000 building tree 46 of 1000 building tree 47 of 1000 building tree 48 of 1000 building tree 49 of 1000 building tree 50 of 1000 building tree 51 of 1000 building tree 52 of 1000 building tree 53 of 1000 building tree 54 of 1000 building tree 55 of 1000 building tree 56 of 1000 building tree 57 of 1000 building tree 58 of 1000 building tree 59 of 1000 building tree 60 of 1000
[Parallel(n_jobs=-1)]: Done 48 tasks | elapsed: 2.0min
building tree 61 of 1000 building tree 62 of 1000 building tree 63 of 1000 building tree 64 of 1000 building tree 65 of 1000 building tree 66 of 1000 building tree 67 of 1000 building tree 68 of 1000 building tree 69 of 1000 building tree 70 of 1000 building tree 71 of 1000 building tree 72 of 1000 building tree 73 of 1000 building tree 74 of 1000 building tree 75 of 1000 building tree 76 of 1000 building tree 77 of 1000 building tree 78 of 1000 building tree 79 of 1000 building tree 80 of 1000 building tree 81 of 1000 building tree 82 of 1000 building tree 83 of 1000 building tree 84 of 1000 building tree 85 of 1000 building tree 86 of 1000 building tree 87 of 1000 building tree 88 of 1000 building tree 89 of 1000 building tree 90 of 1000 building tree 91 of 1000 building tree 92 of 1000 building tree 93 of 1000 building tree 94 of 1000 building tree 95 of 1000 building tree 96 of 1000 building tree 97 of 1000 building tree 98 of 1000 building tree 99 of 1000 building tree 100 of 1000 building tree 101 of 1000 building tree 102 of 1000 building tree 103 of 1000 building tree 104 of 1000 building tree 105 of 1000 building tree 106 of 1000 building tree 107 of 1000 building tree 108 of 1000 building tree 109 of 1000 building tree 110 of 1000 building tree 111 of 1000 building tree 112 of 1000 building tree 113 of 1000 building tree 114 of 1000 building tree 115 of 1000 building tree 116 of 1000 building tree 117 of 1000 building tree 118 of 1000 building tree 119 of 1000 building tree 120 of 1000 building tree 121 of 1000 building tree 122 of 1000 building tree 123 of 1000 building tree 124 of 1000 building tree 125 of 1000 building tree 126 of 1000 building tree 127 of 1000 building tree 128 of 1000 building tree 129 of 1000 building tree 130 of 1000 building tree 131 of 1000 building tree 132 of 1000 building tree 133 of 1000 building tree 134 of 1000 building tree 135 of 1000 building tree 136 of 1000 building tree 137 of 1000 building tree 138 of 1000 building tree 139 of 1000 building tree 140 of 1000 building tree 141 of 1000 building tree 142 of 1000 building tree 143 of 1000 building tree 144 of 1000 building tree 145 of 1000 building tree 146 of 1000 building tree 147 of 1000 building tree 148 of 1000 building tree 149 of 1000 building tree 150 of 1000
[Parallel(n_jobs=-1)]: Done 138 tasks | elapsed: 5.6min
building tree 151 of 1000 building tree 152 of 1000 building tree 153 of 1000 building tree 154 of 1000 building tree 155 of 1000 building tree 156 of 1000 building tree 157 of 1000 building tree 158 of 1000 building tree 159 of 1000 building tree 160 of 1000 building tree 161 of 1000 building tree 162 of 1000 building tree 163 of 1000 building tree 164 of 1000 building tree 165 of 1000 building tree 166 of 1000 building tree 167 of 1000 building tree 168 of 1000 building tree 169 of 1000 building tree 170 of 1000 building tree 171 of 1000 building tree 172 of 1000 building tree 173 of 1000 building tree 174 of 1000 building tree 175 of 1000 building tree 176 of 1000 building tree 177 of 1000 building tree 178 of 1000 building tree 179 of 1000 building tree 180 of 1000 building tree 181 of 1000 building tree 182 of 1000 building tree 183 of 1000 building tree 184 of 1000 building tree 185 of 1000 building tree 186 of 1000 building tree 187 of 1000 building tree 188 of 1000 building tree 189 of 1000 building tree 190 of 1000 building tree 191 of 1000 building tree 192 of 1000 building tree 193 of 1000 building tree 194 of 1000 building tree 195 of 1000 building tree 196 of 1000 building tree 197 of 1000 building tree 198 of 1000 building tree 199 of 1000 building tree 200 of 1000 building tree 201 of 1000 building tree 202 of 1000 building tree 203 of 1000 building tree 204 of 1000 building tree 205 of 1000 building tree 206 of 1000 building tree 207 of 1000 building tree 208 of 1000 building tree 209 of 1000 building tree 210 of 1000 building tree 211 of 1000 building tree 212 of 1000 building tree 213 of 1000 building tree 214 of 1000 building tree 215 of 1000 building tree 216 of 1000 building tree 217 of 1000 building tree 218 of 1000 building tree 219 of 1000 building tree 220 of 1000 building tree 221 of 1000 building tree 222 of 1000 building tree 223 of 1000 building tree 224 of 1000 building tree 225 of 1000 building tree 226 of 1000 building tree 227 of 1000 building tree 228 of 1000 building tree 229 of 1000 building tree 230 of 1000 building tree 231 of 1000 building tree 232 of 1000 building tree 233 of 1000 building tree 234 of 1000 building tree 235 of 1000 building tree 236 of 1000 building tree 237 of 1000 building tree 238 of 1000 building tree 239 of 1000 building tree 240 of 1000 building tree 241 of 1000 building tree 242 of 1000 building tree 243 of 1000 building tree 244 of 1000 building tree 245 of 1000 building tree 246 of 1000 building tree 247 of 1000 building tree 248 of 1000 building tree 249 of 1000 building tree 250 of 1000 building tree 251 of 1000 building tree 252 of 1000 building tree 253 of 1000 building tree 254 of 1000 building tree 255 of 1000 building tree 256 of 1000 building tree 257 of 1000 building tree 258 of 1000 building tree 259 of 1000 building tree 260 of 1000 building tree 261 of 1000 building tree 262 of 1000 building tree 263 of 1000 building tree 264 of 1000 building tree 265 of 1000 building tree 266 of 1000 building tree 267 of 1000 building tree 268 of 1000 building tree 269 of 1000 building tree 270 of 1000 building tree 271 of 1000 building tree 272 of 1000 building tree 273 of 1000 building tree 274 of 1000 building tree 275 of 1000
[Parallel(n_jobs=-1)]: Done 264 tasks | elapsed: 10.3min
building tree 276 of 1000 building tree 277 of 1000 building tree 278 of 1000 building tree 279 of 1000 building tree 280 of 1000 building tree 281 of 1000 building tree 282 of 1000 building tree 283 of 1000 building tree 284 of 1000 building tree 285 of 1000 building tree 286 of 1000 building tree 287 of 1000 building tree 288 of 1000 building tree 289 of 1000 building tree 290 of 1000 building tree 291 of 1000 building tree 292 of 1000 building tree 293 of 1000 building tree 294 of 1000 building tree 295 of 1000 building tree 296 of 1000 building tree 297 of 1000 building tree 298 of 1000 building tree 299 of 1000 building tree 300 of 1000 building tree 301 of 1000 building tree 302 of 1000 building tree 303 of 1000 building tree 304 of 1000 building tree 305 of 1000 building tree 306 of 1000 building tree 307 of 1000 building tree 308 of 1000 building tree 309 of 1000 building tree 310 of 1000 building tree 311 of 1000 building tree 312 of 1000 building tree 313 of 1000 building tree 314 of 1000 building tree 315 of 1000 building tree 316 of 1000 building tree 317 of 1000 building tree 318 of 1000 building tree 319 of 1000 building tree 320 of 1000 building tree 321 of 1000 building tree 322 of 1000 building tree 323 of 1000 building tree 324 of 1000 building tree 325 of 1000 building tree 326 of 1000 building tree 327 of 1000 building tree 328 of 1000 building tree 329 of 1000 building tree 330 of 1000 building tree 331 of 1000 building tree 332 of 1000 building tree 333 of 1000 building tree 334 of 1000 building tree 335 of 1000 building tree 336 of 1000 building tree 337 of 1000 building tree 338 of 1000 building tree 339 of 1000 building tree 340 of 1000 building tree 341 of 1000 building tree 342 of 1000 building tree 343 of 1000 building tree 344 of 1000 building tree 345 of 1000 building tree 346 of 1000 building tree 347 of 1000 building tree 348 of 1000 building tree 349 of 1000 building tree 350 of 1000 building tree 351 of 1000 building tree 352 of 1000 building tree 353 of 1000 building tree 354 of 1000 building tree 355 of 1000 building tree 356 of 1000 building tree 357 of 1000 building tree 358 of 1000 building tree 359 of 1000 building tree 360 of 1000 building tree 361 of 1000 building tree 362 of 1000 building tree 363 of 1000 building tree 364 of 1000 building tree 365 of 1000 building tree 366 of 1000 building tree 367 of 1000 building tree 368 of 1000 building tree 369 of 1000 building tree 370 of 1000 building tree 371 of 1000 building tree 372 of 1000 building tree 373 of 1000 building tree 374 of 1000 building tree 375 of 1000 building tree 376 of 1000 building tree 377 of 1000 building tree 378 of 1000 building tree 379 of 1000 building tree 380 of 1000 building tree 381 of 1000 building tree 382 of 1000 building tree 383 of 1000 building tree 384 of 1000 building tree 385 of 1000 building tree 386 of 1000 building tree 387 of 1000 building tree 388 of 1000 building tree 389 of 1000 building tree 390 of 1000 building tree 391 of 1000 building tree 392 of 1000 building tree 393 of 1000 building tree 394 of 1000 building tree 395 of 1000 building tree 396 of 1000 building tree 397 of 1000 building tree 398 of 1000 building tree 399 of 1000 building tree 400 of 1000 building tree 401 of 1000 building tree 402 of 1000 building tree 403 of 1000 building tree 404 of 1000 building tree 405 of 1000 building tree 406 of 1000 building tree 407 of 1000 building tree 408 of 1000 building tree 409 of 1000 building tree 410 of 1000 building tree 411 of 1000 building tree 412 of 1000 building tree 413 of 1000 building tree 414 of 1000 building tree 415 of 1000 building tree 416 of 1000 building tree 417 of 1000 building tree 418 of 1000 building tree 419 of 1000 building tree 420 of 1000 building tree 421 of 1000 building tree 422 of 1000 building tree 423 of 1000 building tree 424 of 1000 building tree 425 of 1000 building tree 426 of 1000 building tree 427 of 1000 building tree 428 of 1000 building tree 429 of 1000 building tree 430 of 1000 building tree 431 of 1000 building tree 432 of 1000 building tree 433 of 1000 building tree 434 of 1000 building tree 435 of 1000 building tree 436 of 1000 building tree 437 of 1000
[Parallel(n_jobs=-1)]: Done 426 tasks | elapsed: 16.2min
building tree 438 of 1000 building tree 439 of 1000 building tree 440 of 1000 building tree 441 of 1000 building tree 442 of 1000 building tree 443 of 1000 building tree 444 of 1000 building tree 445 of 1000 building tree 446 of 1000 building tree 447 of 1000 building tree 448 of 1000 building tree 449 of 1000 building tree 450 of 1000 building tree 451 of 1000 building tree 452 of 1000 building tree 453 of 1000 building tree 454 of 1000 building tree 455 of 1000 building tree 456 of 1000 building tree 457 of 1000 building tree 458 of 1000 building tree 459 of 1000 building tree 460 of 1000 building tree 461 of 1000 building tree 462 of 1000 building tree 463 of 1000 building tree 464 of 1000 building tree 465 of 1000 building tree 466 of 1000 building tree 467 of 1000 building tree 468 of 1000 building tree 469 of 1000 building tree 470 of 1000 building tree 471 of 1000 building tree 472 of 1000 building tree 473 of 1000 building tree 474 of 1000 building tree 475 of 1000 building tree 476 of 1000 building tree 477 of 1000 building tree 478 of 1000 building tree 479 of 1000 building tree 480 of 1000 building tree 481 of 1000 building tree 482 of 1000 building tree 483 of 1000 building tree 484 of 1000 building tree 485 of 1000 building tree 486 of 1000 building tree 487 of 1000 building tree 488 of 1000 building tree 489 of 1000 building tree 490 of 1000 building tree 491 of 1000 building tree 492 of 1000 building tree 493 of 1000 building tree 494 of 1000 building tree 495 of 1000 building tree 496 of 1000 building tree 497 of 1000 building tree 498 of 1000 building tree 499 of 1000 building tree 500 of 1000 building tree 501 of 1000 building tree 502 of 1000 building tree 503 of 1000 building tree 504 of 1000 building tree 505 of 1000 building tree 506 of 1000 building tree 507 of 1000 building tree 508 of 1000 building tree 509 of 1000 building tree 510 of 1000 building tree 511 of 1000 building tree 512 of 1000 building tree 513 of 1000 building tree 514 of 1000 building tree 515 of 1000 building tree 516 of 1000 building tree 517 of 1000 building tree 518 of 1000 building tree 519 of 1000 building tree 520 of 1000 building tree 521 of 1000 building tree 522 of 1000 building tree 523 of 1000 building tree 524 of 1000 building tree 525 of 1000 building tree 526 of 1000 building tree 527 of 1000 building tree 528 of 1000 building tree 529 of 1000 building tree 530 of 1000 building tree 531 of 1000 building tree 532 of 1000 building tree 533 of 1000 building tree 534 of 1000 building tree 535 of 1000 building tree 536 of 1000 building tree 537 of 1000 building tree 538 of 1000 building tree 539 of 1000 building tree 540 of 1000 building tree 541 of 1000 building tree 542 of 1000 building tree 543 of 1000 building tree 544 of 1000 building tree 545 of 1000 building tree 546 of 1000 building tree 547 of 1000 building tree 548 of 1000 building tree 549 of 1000 building tree 550 of 1000 building tree 551 of 1000 building tree 552 of 1000 building tree 553 of 1000 building tree 554 of 1000 building tree 555 of 1000 building tree 556 of 1000 building tree 557 of 1000 building tree 558 of 1000 building tree 559 of 1000 building tree 560 of 1000 building tree 561 of 1000 building tree 562 of 1000 building tree 563 of 1000 building tree 564 of 1000 building tree 565 of 1000 building tree 566 of 1000 building tree 567 of 1000 building tree 568 of 1000 building tree 569 of 1000 building tree 570 of 1000 building tree 571 of 1000 building tree 572 of 1000 building tree 573 of 1000 building tree 574 of 1000 building tree 575 of 1000 building tree 576 of 1000 building tree 577 of 1000 building tree 578 of 1000 building tree 579 of 1000 building tree 580 of 1000 building tree 581 of 1000 building tree 582 of 1000 building tree 583 of 1000 building tree 584 of 1000 building tree 585 of 1000 building tree 586 of 1000 building tree 587 of 1000 building tree 588 of 1000 building tree 589 of 1000 building tree 590 of 1000 building tree 591 of 1000 building tree 592 of 1000 building tree 593 of 1000 building tree 594 of 1000 building tree 595 of 1000 building tree 596 of 1000 building tree 597 of 1000 building tree 598 of 1000 building tree 599 of 1000 building tree 600 of 1000 building tree 601 of 1000 building tree 602 of 1000 building tree 603 of 1000 building tree 604 of 1000 building tree 605 of 1000 building tree 606 of 1000 building tree 607 of 1000 building tree 608 of 1000 building tree 609 of 1000 building tree 610 of 1000 building tree 611 of 1000 building tree 612 of 1000 building tree 613 of 1000 building tree 614 of 1000 building tree 615 of 1000 building tree 616 of 1000 building tree 617 of 1000 building tree 618 of 1000 building tree 619 of 1000 building tree 620 of 1000 building tree 621 of 1000 building tree 622 of 1000 building tree 623 of 1000 building tree 624 of 1000 building tree 625 of 1000 building tree 626 of 1000 building tree 627 of 1000 building tree 628 of 1000 building tree 629 of 1000 building tree 630 of 1000 building tree 631 of 1000 building tree 632 of 1000 building tree 633 of 1000 building tree 634 of 1000 building tree 635 of 1000
[Parallel(n_jobs=-1)]: Done 624 tasks | elapsed: 23.3min
building tree 636 of 1000 building tree 637 of 1000 building tree 638 of 1000 building tree 639 of 1000 building tree 640 of 1000 building tree 641 of 1000 building tree 642 of 1000 building tree 643 of 1000 building tree 644 of 1000 building tree 645 of 1000 building tree 646 of 1000 building tree 647 of 1000 building tree 648 of 1000 building tree 649 of 1000 building tree 650 of 1000 building tree 651 of 1000 building tree 652 of 1000 building tree 653 of 1000 building tree 654 of 1000 building tree 655 of 1000 building tree 656 of 1000 building tree 657 of 1000 building tree 658 of 1000 building tree 659 of 1000 building tree 660 of 1000 building tree 661 of 1000 building tree 662 of 1000 building tree 663 of 1000 building tree 664 of 1000 building tree 665 of 1000 building tree 666 of 1000 building tree 667 of 1000 building tree 668 of 1000 building tree 669 of 1000 building tree 670 of 1000 building tree 671 of 1000 building tree 672 of 1000 building tree 673 of 1000 building tree 674 of 1000 building tree 675 of 1000 building tree 676 of 1000 building tree 677 of 1000 building tree 678 of 1000 building tree 679 of 1000 building tree 680 of 1000 building tree 681 of 1000 building tree 682 of 1000 building tree 683 of 1000 building tree 684 of 1000 building tree 685 of 1000 building tree 686 of 1000 building tree 687 of 1000 building tree 688 of 1000 building tree 689 of 1000 building tree 690 of 1000 building tree 691 of 1000 building tree 692 of 1000 building tree 693 of 1000 building tree 694 of 1000 building tree 695 of 1000 building tree 696 of 1000 building tree 697 of 1000 building tree 698 of 1000 building tree 699 of 1000 building tree 700 of 1000 building tree 701 of 1000 building tree 702 of 1000 building tree 703 of 1000 building tree 704 of 1000 building tree 705 of 1000 building tree 706 of 1000 building tree 707 of 1000 building tree 708 of 1000 building tree 709 of 1000 building tree 710 of 1000 building tree 711 of 1000 building tree 712 of 1000 building tree 713 of 1000 building tree 714 of 1000 building tree 715 of 1000 building tree 716 of 1000 building tree 717 of 1000 building tree 718 of 1000 building tree 719 of 1000 building tree 720 of 1000 building tree 721 of 1000 building tree 722 of 1000 building tree 723 of 1000 building tree 724 of 1000 building tree 725 of 1000 building tree 726 of 1000 building tree 727 of 1000 building tree 728 of 1000 building tree 729 of 1000 building tree 730 of 1000 building tree 731 of 1000 building tree 732 of 1000 building tree 733 of 1000 building tree 734 of 1000 building tree 735 of 1000 building tree 736 of 1000 building tree 737 of 1000 building tree 738 of 1000 building tree 739 of 1000 building tree 740 of 1000 building tree 741 of 1000 building tree 742 of 1000 building tree 743 of 1000 building tree 744 of 1000 building tree 745 of 1000 building tree 746 of 1000 building tree 747 of 1000 building tree 748 of 1000 building tree 749 of 1000 building tree 750 of 1000 building tree 751 of 1000 building tree 752 of 1000 building tree 753 of 1000 building tree 754 of 1000 building tree 755 of 1000 building tree 756 of 1000 building tree 757 of 1000 building tree 758 of 1000 building tree 759 of 1000 building tree 760 of 1000 building tree 761 of 1000 building tree 762 of 1000 building tree 763 of 1000 building tree 764 of 1000 building tree 765 of 1000 building tree 766 of 1000 building tree 767 of 1000 building tree 768 of 1000 building tree 769 of 1000 building tree 770 of 1000 building tree 771 of 1000 building tree 772 of 1000 building tree 773 of 1000 building tree 774 of 1000 building tree 775 of 1000 building tree 776 of 1000 building tree 777 of 1000 building tree 778 of 1000 building tree 779 of 1000 building tree 780 of 1000 building tree 781 of 1000 building tree 782 of 1000 building tree 783 of 1000 building tree 784 of 1000 building tree 785 of 1000 building tree 786 of 1000 building tree 787 of 1000 building tree 788 of 1000 building tree 789 of 1000 building tree 790 of 1000 building tree 791 of 1000 building tree 792 of 1000 building tree 793 of 1000 building tree 794 of 1000 building tree 795 of 1000 building tree 796 of 1000 building tree 797 of 1000 building tree 798 of 1000 building tree 799 of 1000 building tree 800 of 1000 building tree 801 of 1000 building tree 802 of 1000 building tree 803 of 1000 building tree 804 of 1000 building tree 805 of 1000 building tree 806 of 1000 building tree 807 of 1000 building tree 808 of 1000 building tree 809 of 1000 building tree 810 of 1000 building tree 811 of 1000 building tree 812 of 1000 building tree 813 of 1000 building tree 814 of 1000 building tree 815 of 1000 building tree 816 of 1000 building tree 817 of 1000 building tree 818 of 1000 building tree 819 of 1000 building tree 820 of 1000 building tree 821 of 1000 building tree 822 of 1000 building tree 823 of 1000 building tree 824 of 1000 building tree 825 of 1000 building tree 826 of 1000 building tree 827 of 1000 building tree 828 of 1000 building tree 829 of 1000 building tree 830 of 1000 building tree 831 of 1000 building tree 832 of 1000 building tree 833 of 1000 building tree 834 of 1000 building tree 835 of 1000 building tree 836 of 1000 building tree 837 of 1000 building tree 838 of 1000 building tree 839 of 1000 building tree 840 of 1000 building tree 841 of 1000 building tree 842 of 1000 building tree 843 of 1000 building tree 844 of 1000 building tree 845 of 1000 building tree 846 of 1000 building tree 847 of 1000 building tree 848 of 1000 building tree 849 of 1000 building tree 850 of 1000 building tree 851 of 1000 building tree 852 of 1000 building tree 853 of 1000 building tree 854 of 1000 building tree 855 of 1000 building tree 856 of 1000 building tree 857 of 1000 building tree 858 of 1000 building tree 859 of 1000 building tree 860 of 1000 building tree 861 of 1000 building tree 862 of 1000 building tree 863 of 1000 building tree 864 of 1000 building tree 865 of 1000 building tree 866 of 1000 building tree 867 of 1000 building tree 868 of 1000 building tree 869 of 1000
[Parallel(n_jobs=-1)]: Done 858 tasks | elapsed: 31.8min
building tree 870 of 1000 building tree 871 of 1000 building tree 872 of 1000 building tree 873 of 1000 building tree 874 of 1000 building tree 875 of 1000 building tree 876 of 1000 building tree 877 of 1000 building tree 878 of 1000 building tree 879 of 1000 building tree 880 of 1000 building tree 881 of 1000 building tree 882 of 1000 building tree 883 of 1000 building tree 884 of 1000 building tree 885 of 1000 building tree 886 of 1000 building tree 887 of 1000 building tree 888 of 1000 building tree 889 of 1000 building tree 890 of 1000 building tree 891 of 1000 building tree 892 of 1000 building tree 893 of 1000 building tree 894 of 1000 building tree 895 of 1000 building tree 896 of 1000 building tree 897 of 1000 building tree 898 of 1000 building tree 899 of 1000 building tree 900 of 1000 building tree 901 of 1000 building tree 902 of 1000 building tree 903 of 1000 building tree 904 of 1000 building tree 905 of 1000 building tree 906 of 1000 building tree 907 of 1000 building tree 908 of 1000 building tree 909 of 1000 building tree 910 of 1000 building tree 911 of 1000 building tree 912 of 1000 building tree 913 of 1000 building tree 914 of 1000 building tree 915 of 1000 building tree 916 of 1000 building tree 917 of 1000 building tree 918 of 1000 building tree 919 of 1000 building tree 920 of 1000 building tree 921 of 1000 building tree 922 of 1000 building tree 923 of 1000 building tree 924 of 1000 building tree 925 of 1000 building tree 926 of 1000 building tree 927 of 1000 building tree 928 of 1000 building tree 929 of 1000 building tree 930 of 1000 building tree 931 of 1000 building tree 932 of 1000 building tree 933 of 1000 building tree 934 of 1000 building tree 935 of 1000 building tree 936 of 1000 building tree 937 of 1000 building tree 938 of 1000 building tree 939 of 1000 building tree 940 of 1000 building tree 941 of 1000 building tree 942 of 1000 building tree 943 of 1000 building tree 944 of 1000 building tree 945 of 1000 building tree 946 of 1000 building tree 947 of 1000 building tree 948 of 1000 building tree 949 of 1000 building tree 950 of 1000 building tree 951 of 1000 building tree 952 of 1000 building tree 953 of 1000 building tree 954 of 1000 building tree 955 of 1000 building tree 956 of 1000 building tree 957 of 1000 building tree 958 of 1000 building tree 959 of 1000 building tree 960 of 1000 building tree 961 of 1000 building tree 962 of 1000 building tree 963 of 1000 building tree 964 of 1000 building tree 965 of 1000 building tree 966 of 1000 building tree 967 of 1000 building tree 968 of 1000 building tree 969 of 1000 building tree 970 of 1000 building tree 971 of 1000 building tree 972 of 1000 building tree 973 of 1000 building tree 974 of 1000 building tree 975 of 1000 building tree 976 of 1000 building tree 977 of 1000 building tree 978 of 1000 building tree 979 of 1000 building tree 980 of 1000 building tree 981 of 1000 building tree 982 of 1000 building tree 983 of 1000 building tree 984 of 1000 building tree 985 of 1000 building tree 986 of 1000 building tree 987 of 1000 building tree 988 of 1000 building tree 989 of 1000 building tree 990 of 1000 building tree 991 of 1000 building tree 992 of 1000 building tree 993 of 1000 building tree 994 of 1000 building tree 995 of 1000 building tree 996 of 1000 building tree 997 of 1000 building tree 998 of 1000 building tree 999 of 1000 building tree 1000 of 1000
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 36.7min finished
RandomForestClassifier(bootstrap=False, class_weight=None, criterion='entropy',
max_depth=50, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=2, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=-1, oob_score=False, random_state=None, verbose=5,
warm_start=False)
_jl.dump(best_model, "../models/best_Random_Forest_2.joblib")
['../models/best_Random_Forest_2.joblib']
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Allison Park | city_Amherst | city_Aurora | city_Avon | city_Avondale | city_Beachwood | city_Bellevue | city_Belmont | city_Berea | city_Bethel Park | city_Blue Diamond | city_Boulder City | city_Braddock | city_Brampton | city_Brecksville | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklyn | city_Brossard | city_Brunswick | city_Buckeye | city_Calgary | city_Canonsburg | city_Carefree | city_Carnegie | city_Cave Creek | city_Chagrin Falls | city_Champaign | city_Chandler | city_Chardon | city_Charlotte | city_Chesterland | city_Cleveland | city_Clover | city_Concord | city_Coraopolis | city_Cornelius | city_Cuyahoga Falls | city_Davidson | city_Denver | city_Dollard-des-Ormeaux | city_Dorval | city_East York | city_El Mirage | city_Elyria | city_Etobicoke | city_Euclid | city_Fairlawn | city_Fairview Park | city_Fitchburg | city_Fort Mill | city_Fountain Hills | city_Gastonia | city_Gilbert | city_Glendale | city_Goodyear | city_Harrisburg | city_Henderson | city_Highland Heights | city_Homestead | city_Hudson | city_Huntersville | city_Independence | city_Indian Land | city_Indian Trail | city_Irwin | city_Kannapolis | city_Kent | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Laval | city_Laveen | city_Litchfield Park | city_Longueuil | city_Lorain | city_Lyndhurst | city_Macedonia | city_Madison | city_Maple | city_Markham | city_Matthews | city_Mayfield Heights | city_McKees Rocks | city_McMurray | city_Medina | city_Mentor | city_Mesa | city_Middleburg Heights | city_Middleton | city_Mint Hill | city_Mississauga | city_Monona | city_Monroe | city_Monroeville | city_Montreal | city_Montréal | city_Moon Township | city_Mooresville | city_Mount Holly | city_Murrysville | city_New Kensington | city_Newmarket | city_North Las Vegas | city_North Olmsted | city_North Ridgeville | city_North Royalton | city_North York | city_Northfield | city_Oakmont | city_Oakville | city_Olmsted Falls | city_Orange | city_Orange Village | city_Other | city_Painesville | city_Paradise Valley | city_Parma | city_Peoria | city_Phoenix | city_Pickering | city_Pineville | city_Pittsburgh | city_Pointe-Claire | city_Queen Creek | city_Richmond Hill | city_Rock Hill | city_Rocky River | city_Saint-Laurent | city_Scarborough | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Solon | city_South Euclid | city_South Las Vegas | city_Spring Valley | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sun City | city_Sun Prairie | city_Surprise | city_Tega Cay | city_Tempe | city_Thornhill | city_Tolleson | city_Toronto | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Urbana | city_Valley View | city_Vaughan | city_Verdun | city_Verona | city_Warrensville Heights | city_Waunakee | city_Waxhaw | city_West Mifflin | city_Westlake | city_Westmount | city_Wexford | city_Whitby | city_Willoughby | city_Woodbridge | city_Woodmere | city_York | categories_ Acai Bowls | categories_ Active Life | categories_ Adult Entertainment | categories_ Afghan | categories_ African | categories_ Airports | categories_ American (New) | categories_ American (Traditional) | categories_ Amusement Parks | categories_ Animal Shelters | categories_ Antiques | categories_ Appliances | categories_ Arabian | categories_ Arcades | categories_ Argentine | categories_ Armenian | categories_ Art Galleries | categories_ Arts & Crafts | categories_ Arts & Entertainment | categories_ Asian Fusion | categories_ Automotive | categories_ Bagels | categories_ Bakeries | categories_ Bangladeshi | categories_ Barbeque | categories_ Barbers | categories_ Bars | categories_ Basque | categories_ Beauty & Spas | categories_ Bed & Breakfast | categories_ Beer | categories_ Belgian | categories_ Beverage Store | categories_ Bistros | categories_ Books | categories_ Botanical Gardens | categories_ Bowling | categories_ Brasseries | categories_ Brazilian | categories_ Breakfast & Brunch | categories_ Breweries | categories_ Brewpubs | categories_ British | categories_ Bubble Tea | categories_ Buffets | categories_ Burgers | categories_ Butcher | categories_ Cafes | categories_ Cafeteria | categories_ Cajun/Creole | categories_ Cambodian | categories_ Canadian (New) | categories_ Candy Stores | categories_ Cantonese | categories_ Car Wash | categories_ Caribbean | categories_ Casinos | categories_ Caterers | categories_ Cheese Shops | categories_ Cheesesteaks | categories_ Chicken Shop | categories_ Chicken Wings | categories_ Chinese | categories_ Chocolatiers & Shops | categories_ Cinema | categories_ 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categories_Cambodian | categories_Canadian (New) | categories_Cantonese | categories_Caribbean | categories_Caterers | categories_Cheesesteaks | categories_Chicken Shop | categories_Chicken Wings | categories_Chinese | categories_Coffee & Tea | categories_Coffee Roasteries | categories_Colombian | categories_Comfort Food | categories_Convenience Stores | categories_Cooking Classes | categories_Creperies | categories_Cuban | categories_Custom Cakes | categories_Day Spas | categories_Delicatessen | categories_Delis | categories_Desserts | categories_Dim Sum | categories_Diners | categories_Dinner Theater | categories_Do-It-Yourself Food | categories_Donuts | categories_Education | categories_Ethiopian | categories_Ethnic Grocery | categories_Event Planning & Services | categories_Falafel | categories_Farmers Market | categories_Fashion | categories_Fast Food | categories_Filipino | categories_Fish & Chips | categories_Fondue | categories_Food Court | categories_Food Delivery Services | categories_Food Stands | categories_Food Tours | categories_Food Trucks | categories_French | categories_Furniture Stores | categories_Gas Stations | categories_Gastropubs | categories_Gelato | categories_German | categories_Gluten-Free | categories_Greek | categories_Grocery | categories_Hair Removal | categories_Hair Salons | categories_Hakka | categories_Halal | categories_Hawaiian | categories_Health & Medical | categories_Himalayan/Nepalese | categories_Home & Garden | categories_Home Decor | categories_Home Services | categories_Hot Dogs | categories_Hot Pot | categories_Hotels | categories_Hotels & Travel | categories_Hungarian | categories_Ice Cream & Frozen Yogurt | categories_Imported Food | categories_Indian | categories_Indonesian | categories_International | categories_International Grocery | categories_Internet Cafes | categories_Irish | categories_Italian | categories_Izakaya | categories_Japanese | categories_Juice Bars & Smoothies | categories_Karaoke | categories_Kebab | categories_Korean | categories_Kosher | categories_Landmarks & Historical Buildings | categories_Laotian | categories_Latin American | categories_Lebanese | categories_Live/Raw Food | categories_Local Flavor | categories_Local Services | categories_Mags | categories_Malaysian | categories_Mediterranean | categories_Mexican | categories_Middle Eastern | categories_Modern European | categories_Mongolian | categories_Moroccan | categories_Music Venues | categories_Nail Salons | categories_New Mexican Cuisine | categories_Nightlife | categories_Noodles | categories_Organic Stores | categories_Other | categories_Outlet Stores | categories_Pakistani | categories_Pan Asian | categories_Party & Event Planning | categories_Patisserie/Cake Shop | categories_Performing Arts | categories_Persian/Iranian | categories_Peruvian | categories_Pets | categories_Pizza | categories_Poke | categories_Polish | categories_Pop-Up Restaurants | categories_Portuguese | categories_Poutineries | categories_Public Markets | categories_Public Services & Government | categories_Ramen | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shaved Snow | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Tacos | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Tex-Mex | categories_Thai | categories_Tobacco Shops | categories_Towing | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wineries | categories_Wraps | |
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| 0 | -j8YU0f5cL_fbnzsi1zkpA | rEsBrt6U7i8O4rC81lV6NQ | --6MefnULPED_I942VcFNA | 4 | 0 | 0 | 0 | 1 | 0.997555 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 3.703313 | 67 | 1 | 1 | 16 | 6 | 2 | 3574 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | -1 | 0.553523 | 1.800000 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 2.875000 | 86 | 0 | 1 | 34 | 9 | 12 | 166 | 16.0 | 3.000000 | 15.0 | 2.909282 | 13.563981 | 1.800000 | 2.20000 | 3.686094 | 4.500000 | 3.333333 | 1.000000 | 3.000000 | 3.933014 | 3.868171 | 3.770015 | 2.000000 | 2.500000 | 3.662669 | 4.500000 | 3.333333 | 1.00000 | 3.000000 | 3.904608 | 3.851784 | 3.744434 | 1.799679 | 2.460018 | 3.678871 | 4.571695 | 3.355656 | 1.000000 | 2.924220 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 1 | 0.990602 | 4.300000 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 4.108108 | 227 | 2 | 7 | 99 | 47 | 30 | 286 | 37.0 | 4.161290 | 31.0 | 4.130733 | 31.326167 | 4.300000 | 3.75000 | 3.686094 | 3.500000 | 4.454545 | 3.666667 | 3.800000 | 3.933014 | 4.000000 | 4.000000 | 4.333333 | 3.750000 | 3.662669 | 3.000000 | 4.555556 | 3.50000 | 4.000000 | 3.904608 | 4.000000 | 4.000000 | 4.299574 | 3.724926 | 3.678871 | 3.340936 | 4.538601 | 3.626374 | 3.946442 | 3.928912 | 4.00000 | 4.000000 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 1 | 0.968214 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 3.703313 | 1 | 0 | 0 | 0 | 0 | 0 | 2110 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 1 | 0.995667 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 59 | 0 | 0 | 5 | 0 | 1 | 22 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 1.8s [Parallel(n_jobs=12)]: Done 138 tasks | elapsed: 4.8s [Parallel(n_jobs=12)]: Done 264 tasks | elapsed: 8.9s [Parallel(n_jobs=12)]: Done 426 tasks | elapsed: 13.3s [Parallel(n_jobs=12)]: Done 624 tasks | elapsed: 18.8s [Parallel(n_jobs=12)]: Done 858 tasks | elapsed: 25.2s [Parallel(n_jobs=12)]: Done 1000 out of 1000 | elapsed: 28.9s finished
predictions: [0 0 1 ... 0 1 0]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Random Forest classifier:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Random Forest Classifier:", _accuracy_score(test_set['likes'], predic)*100)
Report for Random Forest classifier:
precision recall f1-score support
0 0.69 0.40 0.51 50930
1 0.75 0.91 0.83 103063
accuracy 0.74 153993
macro avg 0.72 0.66 0.67 153993
weighted avg 0.73 0.74 0.72 153993
Accuracy for Random Forest Classifier: 74.16765697142078
# Confusion matrix for Random Forest
print("Confusion Matrix for Random Forest: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for Random Forest before balance the data:
array([[20372, 30558],
[ 9222, 93841]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("Random Forest ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
%reset
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Allison Park | city_Amherst | city_Aurora | city_Avon | city_Avondale | city_Beachwood | city_Bellevue | city_Belmont | city_Berea | city_Bethel Park | city_Blue Diamond | city_Boulder City | city_Braddock | city_Brampton | city_Brecksville | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklyn | city_Brossard | city_Brunswick | city_Buckeye | city_Calgary | city_Canonsburg | city_Carefree | city_Carnegie | city_Cave Creek | city_Chagrin Falls | city_Champaign | city_Chandler | city_Chardon | city_Charlotte | city_Chesterland | city_Cleveland | city_Clover | city_Concord | city_Coraopolis | city_Cornelius | city_Cuyahoga Falls | city_Davidson | city_Denver | city_Dollard-des-Ormeaux | city_Dorval | city_East York | city_El Mirage | city_Elyria | city_Etobicoke | city_Euclid | city_Fairlawn | city_Fairview Park | city_Fitchburg | city_Fort Mill | city_Fountain Hills | city_Gastonia | city_Gilbert | city_Glendale | city_Goodyear | city_Harrisburg | city_Henderson | city_Highland Heights | city_Homestead | city_Hudson | city_Huntersville | city_Independence | city_Indian Land | city_Indian Trail | city_Irwin | city_Kannapolis | city_Kent | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Laval | city_Laveen | city_Litchfield Park | city_Longueuil | city_Lorain | city_Lyndhurst | city_Macedonia | city_Madison | city_Maple | city_Markham | city_Matthews | city_Mayfield Heights | city_McKees Rocks | city_McMurray | city_Medina | city_Mentor | city_Mesa | city_Middleburg Heights | city_Middleton | city_Mint Hill | city_Mississauga | city_Monona | city_Monroe | city_Monroeville | city_Montreal | city_Montréal | city_Moon Township | city_Mooresville | city_Mount Holly | city_Murrysville | city_New Kensington | city_Newmarket | city_North Las Vegas | city_North Olmsted | city_North Ridgeville | city_North Royalton | city_North York | city_Northfield | city_Oakmont | city_Oakville | city_Olmsted Falls | city_Orange | city_Orange Village | city_Other | city_Painesville | city_Paradise Valley | city_Parma | city_Peoria | city_Phoenix | city_Pickering | city_Pineville | city_Pittsburgh | city_Pointe-Claire | city_Queen Creek | city_Richmond Hill | city_Rock Hill | city_Rocky River | city_Saint-Laurent | city_Scarborough | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Solon | city_South Euclid | city_South Las Vegas | city_Spring Valley | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sun City | city_Sun Prairie | city_Surprise | city_Tega Cay | city_Tempe | city_Thornhill | city_Tolleson | city_Toronto | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Urbana | city_Valley View | city_Vaughan | city_Verdun | city_Verona | city_Warrensville Heights | city_Waunakee | city_Waxhaw | city_West Mifflin | city_Westlake | city_Westmount | city_Wexford | city_Whitby | city_Willoughby | city_Woodbridge | city_Woodmere | city_York | categories_ Acai Bowls | categories_ Active Life | categories_ Adult Entertainment | categories_ Afghan | categories_ African | categories_ Airports | categories_ American (New) | categories_ American (Traditional) | categories_ Amusement Parks | categories_ Animal Shelters | categories_ Antiques | categories_ Appliances | categories_ Arabian | categories_ Arcades | categories_ Argentine | categories_ Armenian | categories_ Art Galleries | categories_ Arts & Crafts | categories_ Arts & Entertainment | categories_ Asian Fusion | categories_ Automotive | categories_ Bagels | categories_ Bakeries | categories_ Bangladeshi | categories_ Barbeque | 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| categories_Public Markets | categories_Public Services & Government | categories_Ramen | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shaved Snow | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Tacos | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Tex-Mex | categories_Thai | categories_Tobacco Shops | categories_Towing | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wineries | categories_Wraps | |
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| 1 | no4Eo4WloZRLwcYZP9gfhg | m-p-7WuB85UjsLDaxJXCXA | --1UhMGODdWsrMastO9DZw | 5 | 0 | 0 | 0 | 1 | 0.964784 | 4.000000 | 4.000000 | 4.000000 | 3.757630 | 3.808050 | 3.714375 | 1 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.333333 | 18 | 0 | 0 | 1 | 1 | 1 | 454 | 3.0 | 3.333333 | 3.0 | 3.231620 | 2.702970 | 3.556721 | 3.79608 | 4.000000 | 3.777956 | 5.000000 | 2.500000 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 4.000000 | 3.749776 | 5.000000 | 2.50000 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 4.000000 | 3.770170 | 5.000000 | 2.353400 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 8QUwzeXeyJ3L15lKfhKLsQ | Bsy9F-59sl9OT_bvZNl3hA | --1UhMGODdWsrMastO9DZw | 1 | 0 | 0 | 0 | 1 | 0.871544 | 3.000000 | 3.000000 | 2.990709 | 2.926486 | 2.974252 | 2.874015 | 0 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 2.571429 | 27 | 0 | 0 | 31 | 3 | 3 | 46 | 14.0 | 2.692308 | 13.0 | 2.692835 | 12.704916 | 2.500000 | 1.00000 | 3.000000 | 3.777956 | 3.200000 | 3.684951 | 3.789846 | 1.000000 | 2.333333 | 3.770015 | 2.500000 | 1.000000 | 3.000000 | 3.749776 | 3.200000 | 3.66752 | 3.771654 | 1.000000 | 3.000000 | 3.744434 | 2.505665 | 1.000000 | 2.990709 | 3.770170 | 3.204491 | 3.676024 | 3.788204 | 1.000000 | 2.974502 | 3.767263 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | BIecLw546kAlD7kmlk7vXA | sTVGcezME7gYBhIlYtcfpg | --1UhMGODdWsrMastO9DZw | 2 | 0 | 0 | 0 | 1 | 0.988395 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 7778.0 | 51.049673 | -114.079977 | 24 | 4.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 61.0 | 51.0 | 52.0 | 51.0 | 58.0 | 58.0 | 17.0 | 70.0 | 4.227273 | 22.0 | 4.214286 | 14.0 | 4.267477 | 16.978214 | 0.009869 | 3.703313 | 2 | 0 | 0 | 1 | 0 | 0 | 622 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.867280 | 3.767263 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0nVZ9Cg1A1lVA8EFWbt5lg | EisUuXVeVJN_FcFiE-tqwA | --6MefnULPED_I942VcFNA | 3 | 2 | 0 | 0 | 1 | 0.927789 | 2.966667 | 2.923077 | 2.954478 | 2.985799 | 2.973742 | 2.915633 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.000000 | 2.980769 | 212 | 3 | 23 | 307 | 157 | 71 | 2902 | 104.0 | 3.000000 | 88.0 | 2.997169 | 88.020993 | 2.966667 | 4.00000 | 3.686094 | 2.000000 | 3.040000 | 2.600000 | 3.600000 | 2.500000 | 1.000000 | 3.750000 | 2.923077 | 4.000000 | 3.662669 | 1.666667 | 3.146341 | 2.60000 | 3.750000 | 2.500000 | 1.000000 | 3.666667 | 2.954478 | 4.000000 | 3.678871 | 1.826533 | 3.115334 | 2.630356 | 3.621347 | 2.500236 | 1.000000 | 3.828739 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Rule for calculating the number of hidden layers (from here):
$N_h = \frac {N_s} {(\alpha * (N_i + N_o))}$
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 6
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
119
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 206s 527us/step - loss: 0.6102 - acc: 0.6933 - val_loss: 0.5960 - val_acc: 0.7106 Epoch 2/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5606 - acc: 0.7197 - val_loss: 0.5684 - val_acc: 0.7193 Epoch 3/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5503 - acc: 0.7273 - val_loss: 0.5508 - val_acc: 0.7308 Epoch 4/100 390870/390870 [==============================] - 24s 63us/step - loss: 0.5454 - acc: 0.7317 - val_loss: 0.5547 - val_acc: 0.7287 Epoch 5/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5414 - acc: 0.7337 - val_loss: 0.5375 - val_acc: 0.7373 Epoch 6/100 390870/390870 [==============================] - 25s 63us/step - loss: 0.5393 - acc: 0.7350 - val_loss: 0.5453 - val_acc: 0.7272 Epoch 7/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5381 - acc: 0.7366 - val_loss: 0.5348 - val_acc: 0.7402 Epoch 8/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5354 - acc: 0.7380 - val_loss: 0.5390 - val_acc: 0.7395 Epoch 9/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5340 - acc: 0.7397 - val_loss: 0.5313 - val_acc: 0.7390 Epoch 10/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5322 - acc: 0.7405 - val_loss: 0.5354 - val_acc: 0.7448 Epoch 11/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5326 - acc: 0.7408 - val_loss: 0.5372 - val_acc: 0.7415 Epoch 12/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5298 - acc: 0.7423 - val_loss: 0.5361 - val_acc: 0.7384 Epoch 13/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5300 - acc: 0.7420 - val_loss: 0.5407 - val_acc: 0.7356 Epoch 14/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5290 - acc: 0.7425 - val_loss: 0.5250 - val_acc: 0.7481 Epoch 15/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5274 - acc: 0.7433 - val_loss: 0.5267 - val_acc: 0.7436 Epoch 16/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5255 - acc: 0.7449 - val_loss: 0.5217 - val_acc: 0.7494 Epoch 17/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5254 - acc: 0.7451 - val_loss: 0.5313 - val_acc: 0.7460 Epoch 18/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5241 - acc: 0.7460 - val_loss: 0.5246 - val_acc: 0.7471 Epoch 19/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5240 - acc: 0.7460 - val_loss: 0.5237 - val_acc: 0.7465 Epoch 20/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5234 - acc: 0.7461 - val_loss: 0.5250 - val_acc: 0.7481 Epoch 21/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5226 - acc: 0.7462 - val_loss: 0.5417 - val_acc: 0.7359 Epoch 22/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5234 - acc: 0.7465 - val_loss: 0.5258 - val_acc: 0.7474 Epoch 23/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5222 - acc: 0.7472 - val_loss: 0.5217 - val_acc: 0.7499 Epoch 24/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5210 - acc: 0.7478 - val_loss: 0.5267 - val_acc: 0.7442 Epoch 25/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5207 - acc: 0.7484 - val_loss: 0.5455 - val_acc: 0.7354 Epoch 26/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5202 - acc: 0.7486 - val_loss: 0.5288 - val_acc: 0.7449 Epoch 27/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5214 - acc: 0.7476 - val_loss: 0.5456 - val_acc: 0.7338 Epoch 28/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5209 - acc: 0.7484 - val_loss: 0.5258 - val_acc: 0.7468 Epoch 29/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5204 - acc: 0.7472 - val_loss: 0.5231 - val_acc: 0.7481 Epoch 30/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5190 - acc: 0.7493 - val_loss: 0.5230 - val_acc: 0.7463 Epoch 31/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5189 - acc: 0.7490 - val_loss: 0.5250 - val_acc: 0.7480 Epoch 32/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5205 - acc: 0.7482 - val_loss: 0.5226 - val_acc: 0.7496 Epoch 33/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5202 - acc: 0.7486 - val_loss: 0.5276 - val_acc: 0.7457 Epoch 34/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5172 - acc: 0.7503 - val_loss: 0.5243 - val_acc: 0.7481 Epoch 35/100 390870/390870 [==============================] - 25s 63us/step - loss: 0.5173 - acc: 0.7503 - val_loss: 0.5219 - val_acc: 0.7481 Epoch 36/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5174 - acc: 0.7494 - val_loss: 0.5212 - val_acc: 0.7479 Epoch 37/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5172 - acc: 0.7500 - val_loss: 0.5314 - val_acc: 0.7419 Epoch 38/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5190 - acc: 0.7495 - val_loss: 0.5206 - val_acc: 0.7498 Epoch 39/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5170 - acc: 0.7502 - val_loss: 0.5233 - val_acc: 0.7471 Epoch 40/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5176 - acc: 0.7498 - val_loss: 0.5207 - val_acc: 0.7506 Epoch 41/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5187 - acc: 0.7491 - val_loss: 0.5242 - val_acc: 0.7474 Epoch 42/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5160 - acc: 0.7508 - val_loss: 0.5182 - val_acc: 0.7502 Epoch 43/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5154 - acc: 0.7512 - val_loss: 0.5214 - val_acc: 0.7492 Epoch 44/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5152 - acc: 0.7511 - val_loss: 0.5259 - val_acc: 0.7432 Epoch 45/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5155 - acc: 0.7511 - val_loss: 0.5198 - val_acc: 0.7497 Epoch 46/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5165 - acc: 0.7506 - val_loss: 0.5294 - val_acc: 0.7464 Epoch 47/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5163 - acc: 0.7510 - val_loss: 0.5256 - val_acc: 0.7462 Epoch 48/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5156 - acc: 0.7510 - val_loss: 0.5188 - val_acc: 0.7501 Epoch 49/100 390870/390870 [==============================] - 23s 60us/step - loss: 0.5160 - acc: 0.7499 - val_loss: 0.5257 - val_acc: 0.7473 Epoch 50/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5167 - acc: 0.7518 - val_loss: 0.5215 - val_acc: 0.7491 Epoch 51/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5146 - acc: 0.7513 - val_loss: 0.5201 - val_acc: 0.7514 Epoch 52/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5137 - acc: 0.7524 - val_loss: 0.5248 - val_acc: 0.7475 Epoch 53/100 390870/390870 [==============================] - 23s 60us/step - loss: 0.5141 - acc: 0.7523 - val_loss: 0.5217 - val_acc: 0.7475 Epoch 54/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5132 - acc: 0.7525 - val_loss: 0.5188 - val_acc: 0.7510 Epoch 55/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5134 - acc: 0.7523 - val_loss: 0.5183 - val_acc: 0.7523 Epoch 56/100 390870/390870 [==============================] - 23s 60us/step - loss: 0.5128 - acc: 0.7526 - val_loss: 0.5206 - val_acc: 0.7486 Epoch 57/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5130 - acc: 0.7525 - val_loss: 0.5402 - val_acc: 0.7370 Epoch 58/100 390870/390870 [==============================] - 24s 60us/step - loss: 0.5136 - acc: 0.7521 - val_loss: 0.5204 - val_acc: 0.7499 Epoch 59/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5135 - acc: 0.7517 - val_loss: 0.5186 - val_acc: 0.7510 Epoch 60/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5132 - acc: 0.7522 - val_loss: 0.5194 - val_acc: 0.7507 Epoch 61/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5131 - acc: 0.7523 - val_loss: 0.5266 - val_acc: 0.7503 Epoch 62/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5115 - acc: 0.7538 - val_loss: 0.5164 - val_acc: 0.7518 Epoch 63/100 390870/390870 [==============================] - 24s 61us/step - loss: 0.5140 - acc: 0.7522 - val_loss: 0.5255 - val_acc: 0.7511 Epoch 64/100 390870/390870 [==============================] - 24s 62us/step - loss: 0.5138 - acc: 0.7515 - val_loss: 0.5199 - val_acc: 0.7496 Epoch 65/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5141 - acc: 0.7517 - val_loss: 0.5218 - val_acc: 0.7502 Epoch 66/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5135 - acc: 0.7525 - val_loss: 0.5220 - val_acc: 0.7477 Epoch 67/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5152 - acc: 0.7519 - val_loss: 0.5243 - val_acc: 0.7497 Epoch 68/100 390870/390870 [==============================] - 25s 63us/step - loss: 0.5137 - acc: 0.7518 - val_loss: 0.5184 - val_acc: 0.7512 Epoch 69/100 390870/390870 [==============================] - 25s 63us/step - loss: 0.5174 - acc: 0.7515 - val_loss: 0.5292 - val_acc: 0.7474 Epoch 70/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5138 - acc: 0.7516 - val_loss: 0.5265 - val_acc: 0.7449 Epoch 71/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5127 - acc: 0.7534 - val_loss: 0.5263 - val_acc: 0.7479 Epoch 72/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5125 - acc: 0.7533 - val_loss: 0.5214 - val_acc: 0.7481 Epoch 73/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5119 - acc: 0.7531 - val_loss: 0.5209 - val_acc: 0.7503 Epoch 74/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5123 - acc: 0.7526 - val_loss: 0.5200 - val_acc: 0.7488 Epoch 75/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5109 - acc: 0.7536 - val_loss: 0.5184 - val_acc: 0.7513 Epoch 76/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5133 - acc: 0.7517 - val_loss: 0.5296 - val_acc: 0.7484 Epoch 77/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5128 - acc: 0.7520 - val_loss: 0.5254 - val_acc: 0.7467 Epoch 78/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5124 - acc: 0.7530 - val_loss: 0.5206 - val_acc: 0.7495 Epoch 79/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5120 - acc: 0.7534 - val_loss: 0.5209 - val_acc: 0.7489 Epoch 80/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5106 - acc: 0.7540 - val_loss: 0.5274 - val_acc: 0.7467 Epoch 81/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5137 - acc: 0.7519 - val_loss: 0.5233 - val_acc: 0.7467 Epoch 82/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5141 - acc: 0.7507 - val_loss: 0.5208 - val_acc: 0.7471 Epoch 83/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5109 - acc: 0.7538 - val_loss: 0.5199 - val_acc: 0.7525 Epoch 84/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5109 - acc: 0.7531 - val_loss: 0.5244 - val_acc: 0.7486 Epoch 85/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5119 - acc: 0.7528 - val_loss: 0.5202 - val_acc: 0.7500 Epoch 86/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5106 - acc: 0.7541 - val_loss: 0.5189 - val_acc: 0.7506 Epoch 87/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5110 - acc: 0.7535 - val_loss: 0.5202 - val_acc: 0.7517 Epoch 88/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5114 - acc: 0.7532 - val_loss: 0.5246 - val_acc: 0.7474 Epoch 89/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5105 - acc: 0.7543 - val_loss: 0.5199 - val_acc: 0.7481 Epoch 90/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5123 - acc: 0.7527 - val_loss: 0.5172 - val_acc: 0.7505 Epoch 91/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5119 - acc: 0.7523 - val_loss: 0.5270 - val_acc: 0.7457 Epoch 92/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5116 - acc: 0.7524 - val_loss: 0.5169 - val_acc: 0.7524 Epoch 93/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5094 - acc: 0.7547 - val_loss: 0.5164 - val_acc: 0.7524 Epoch 94/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5084 - acc: 0.7551 - val_loss: 0.5171 - val_acc: 0.7507 Epoch 95/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5084 - acc: 0.7552 - val_loss: 0.5224 - val_acc: 0.7516 Epoch 96/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5106 - acc: 0.7539 - val_loss: 0.5236 - val_acc: 0.7490 Epoch 97/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5095 - acc: 0.7540 - val_loss: 0.5454 - val_acc: 0.7475 Epoch 98/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5096 - acc: 0.7538 - val_loss: 0.5237 - val_acc: 0.7514 Epoch 99/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5099 - acc: 0.7541 - val_loss: 0.5264 - val_acc: 0.7490 Epoch 100/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5098 - acc: 0.7541 - val_loss: 0.5324 - val_acc: 0.7484
<keras.callbacks.History at 0x234bf5d0908>
# Save trained model
classifier.save("../models/trained_deep_neural_network.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 16s 29us/step [0.516120845819726, 0.7530937380230908]
The neural network gives us the probability that the class is 1, we binarize with threshold to 0.5 thus having the labels with probability up to 0.5 at 0, the others at 1. We could use two nerones in output thus having a probability vector expressing the probability that it is 1 and the probability that it is 0, but only an equivalent approach would result.
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set.head()
| review_id | user_id | business_id | stars_review | useful_review | funny_review | cool_review | bin_truth_score | real_truth_score | cuisine_av_hist | cuisine_av_hist_bin | cuisine_av_hist_real | coll_score | coll_score_bin | coll_score_real | likes | postal_code | latitude | longitude | review_count | stars_restaurant | Monday_Open | Tuesday_Open | Wednesday_Open | Thursday_Open | Friday_Open | Saturday_Open | Sunday_Open | Monday_Close | Tuesday_Close | Wednesday_Close | Thursday_Close | Friday_Close | Saturday_Close | Sunday_Close | average_stars_review | num_reviews_review | average_stars_bin_review | num_reviews_bin_review | average_stars_real_review | num_reviews_real_review | compliment_count | average_stars_user | review | years_of_elite | fans | useful_user | cool_user | funny_user | friends | num_reviews_user | average_stars_bin_user | num_reviews_bin_user | average_stars_real_user | num_reviews_real_user | av_rat_chinese_cuisine | av_rat_japanese_cuisine | av_rat_mexican_cuisine | av_rat_italian_cuisine | av_rat_others_cuisine | av_rat_american_cuisine | av_rat_korean_cuisine | av_rat_mediterranean_cuisine | av_rat_thai_cuisine | av_rat_asianfusion_cuisine | av_rat_chinese_cuisine_bin | av_rat_japanese_cuisine_bin | av_rat_mexican_cuisine_bin | av_rat_italian_cuisine_bin | av_rat_others_cuisine_bin | av_rat_american_cuisine_bin | av_rat_korean_cuisine_bin | av_rat_mediterranean_cuisine_bin | av_rat_thai_cuisine_bin | av_rat_asianfusion_cuisine_bin | av_rat_chinese_cuisine_real | av_rat_japanese_cuisine_real | av_rat_mexican_cuisine_real | av_rat_italian_cuisine_real | av_rat_others_cuisine_real | av_rat_american_cuisine_real | av_rat_korean_cuisine_real | av_rat_mediterranean_cuisine_real | av_rat_thai_cuisine_real | av_rat_asianfusion_cuisine_real | OutdoorSeating_False | OutdoorSeating_None | OutdoorSeating_True | BusinessAcceptsCreditCards_False | BusinessAcceptsCreditCards_None | BusinessAcceptsCreditCards_True | RestaurantsDelivery_False | RestaurantsDelivery_None | RestaurantsDelivery_True | RestaurantsReservations_False | RestaurantsReservations_None | RestaurantsReservations_True | WiFi_Free | WiFi_No | WiFi_None | WiFi_Paid | Alcohol_Beer&Wine | Alcohol_Full_Bar | Alcohol_No | Alcohol_None | city_Ahwatukee | city_Airdrie | city_Ajax | city_Akron | city_Allison Park | city_Amherst | city_Aurora | city_Avon | city_Avondale | city_Beachwood | city_Bellevue | city_Belmont | city_Berea | city_Bethel Park | city_Blue Diamond | city_Boulder City | city_Braddock | city_Brampton | city_Brecksville | city_Bridgeville | city_Broadview Heights | city_Brook Park | city_Brooklyn | city_Brossard | city_Brunswick | city_Buckeye | city_Calgary | city_Canonsburg | city_Carefree | city_Carnegie | city_Cave Creek | city_Chagrin Falls | city_Champaign | city_Chandler | city_Chardon | city_Charlotte | city_Chesterland | city_Cleveland | city_Clover | city_Concord | city_Coraopolis | city_Cornelius | city_Cuyahoga Falls | city_Davidson | city_Denver | city_Dollard-des-Ormeaux | city_Dorval | city_East York | city_El Mirage | city_Elyria | city_Etobicoke | city_Euclid | city_Fairlawn | city_Fairview Park | city_Fitchburg | city_Fort Mill | city_Fountain Hills | city_Gastonia | city_Gilbert | city_Glendale | city_Goodyear | city_Harrisburg | city_Henderson | city_Highland Heights | city_Homestead | city_Hudson | city_Huntersville | city_Independence | city_Indian Land | city_Indian Trail | city_Irwin | city_Kannapolis | city_Kent | city_Lake Wylie | city_Lakewood | city_Las Vegas | city_Laval | city_Laveen | city_Litchfield Park | city_Longueuil | city_Lorain | city_Lyndhurst | city_Macedonia | city_Madison | city_Maple | city_Markham | city_Matthews | city_Mayfield Heights | city_McKees Rocks | city_McMurray | city_Medina | city_Mentor | city_Mesa | city_Middleburg Heights | city_Middleton | city_Mint Hill | city_Mississauga | city_Monona | city_Monroe | city_Monroeville | city_Montreal | city_Montréal | city_Moon Township | city_Mooresville | city_Mount Holly | city_Murrysville | city_New Kensington | city_Newmarket | city_North Las Vegas | city_North Olmsted | city_North Ridgeville | city_North Royalton | city_North York | city_Northfield | city_Oakmont | city_Oakville | city_Olmsted Falls | city_Orange | city_Orange Village | city_Other | city_Painesville | city_Paradise Valley | city_Parma | city_Peoria | city_Phoenix | city_Pickering | city_Pineville | city_Pittsburgh | city_Pointe-Claire | city_Queen Creek | city_Richmond Hill | city_Rock Hill | city_Rocky River | city_Saint-Laurent | city_Scarborough | city_Scottsdale | city_Seven Hills | city_Sewickley | city_Solon | city_South Euclid | city_South Las Vegas | city_Spring Valley | city_Stoughton | city_Stow | city_Streetsboro | city_Strongsville | city_Sun City | city_Sun Prairie | city_Surprise | city_Tega Cay | city_Tempe | city_Thornhill | city_Tolleson | city_Toronto | city_Twinsburg | city_Unionville | city_University Heights | city_Upper Saint Clair | city_Urbana | city_Valley View | city_Vaughan | city_Verdun | city_Verona | city_Warrensville Heights | city_Waunakee | city_Waxhaw | city_West Mifflin | city_Westlake | city_Westmount | city_Wexford | city_Whitby | city_Willoughby | city_Woodbridge | city_Woodmere | city_York | categories_ Acai Bowls | categories_ Active Life | categories_ Adult Entertainment | categories_ Afghan | categories_ African | categories_ Airports | categories_ American (New) | categories_ American (Traditional) | categories_ Amusement Parks | categories_ Animal Shelters | categories_ Antiques | categories_ Appliances | categories_ Arabian | categories_ Arcades | categories_ Argentine | categories_ Armenian | categories_ Art Galleries | categories_ Arts & Crafts | categories_ Arts & Entertainment | categories_ Asian Fusion | categories_ Automotive | categories_ Bagels | categories_ Bakeries | categories_ Bangladeshi | categories_ Barbeque | categories_ Barbers | categories_ Bars | categories_ Basque | categories_ Beauty & Spas | categories_ Bed & Breakfast | categories_ Beer | categories_ Belgian | categories_ Beverage Store | categories_ Bistros | categories_ Books | categories_ Botanical Gardens | categories_ Bowling | categories_ Brasseries | categories_ Brazilian | categories_ Breakfast & Brunch | categories_ Breweries | categories_ Brewpubs | categories_ British | categories_ Bubble Tea | categories_ Buffets | categories_ Burgers | categories_ Butcher | categories_ Cafes | categories_ Cafeteria | categories_ Cajun/Creole | categories_ Cambodian | categories_ Canadian (New) | categories_ Candy Stores | categories_ Cantonese | categories_ Car Wash | categories_ Caribbean | categories_ Casinos | categories_ Caterers | categories_ Cheese Shops | categories_ Cheesesteaks | categories_ Chicken Shop | categories_ Chicken Wings | categories_ Chinese | categories_ Chocolatiers & Shops | categories_ Cinema | categories_ Cocktail Bars | categories_ Coffee & Tea | categories_ Coffee Roasteries | categories_ Colombian | categories_ Comfort Food | categories_ Community Service/Non-Profit | categories_ Convenience Stores | categories_ Conveyor Belt Sushi | categories_ Cooking Classes | categories_ Country Dance Halls | categories_ Creperies | categories_ Cuban | categories_ Cupcakes | categories_ Custom Cakes | categories_ Dance Clubs | categories_ Day Spas | categories_ Delicatessen | categories_ Delis | categories_ Department Stores | categories_ Desserts | categories_ Dim Sum | categories_ Diners | categories_ Dinner Theater | categories_ Distilleries | categories_ Dive Bars | categories_ Do-It-Yourself Food | categories_ Dominican | categories_ Donairs | categories_ Donuts | categories_ Drugstores | categories_ Eatertainment | categories_ Education | categories_ Egyptian | categories_ Empanadas | categories_ Employment Agencies | categories_ Ethiopian | categories_ Ethnic Food | categories_ Ethnic Grocery | categories_ Event Planning & Services | categories_ Eyelash Service | categories_ Falafel | categories_ Farmers Market | categories_ Fashion | categories_ Fast Food | categories_ Festivals | categories_ Filipino | categories_ Fish & Chips | categories_ Fitness & Instruction | categories_ Florists | categories_ Flowers & Gifts | categories_ Fondue | categories_ Food Court | categories_ Food Delivery Services | categories_ Food Stands | categories_ Food Tours | categories_ Food Trucks | categories_ French | categories_ Fruits & Veggies | categories_ Furniture Stores | categories_ Gas Stations | categories_ Gastropubs | categories_ Gay Bars | categories_ Gelato | categories_ German | categories_ Gift Shops | categories_ Gluten-Free | categories_ Golf | categories_ Greek | categories_ Grocery | categories_ Guamanian | categories_ Hair Removal | categories_ Hair Salons | categories_ Hakka | categories_ Halal | categories_ Hawaiian | categories_ Health & Medical | categories_ Health Markets | categories_ Himalayan/Nepalese | categories_ Home & Garden | categories_ Home Decor | categories_ Home Services | categories_ Hong Kong Style Cafe | categories_ Hookah Bars | categories_ Hostels | categories_ Hot Dogs | categories_ Hot Pot | categories_ Hotels | categories_ Hotels & Travel | categories_ Hungarian | categories_ Ice Cream & Frozen Yogurt | categories_ Imported Food | categories_ Indian | categories_ Indonesian | categories_ International | categories_ International Grocery | categories_ Internet Cafes | categories_ Irish | categories_ Italian | categories_ Izakaya | categories_ Japanese | categories_ Jazz & Blues | categories_ Juice Bars & Smoothies | categories_ Karaoke | categories_ Kebab | categories_ Kids Activities | categories_ Kitchen & Bath | categories_ Kombucha | categories_ Korean | categories_ Kosher | categories_ Landmarks & Historical Buildings | categories_ Laotian | categories_ Latin American | categories_ Lebanese | categories_ Live/Raw Food | categories_ Local Flavor | categories_ Local Services | categories_ Lounges | categories_ Macarons | categories_ Mags | categories_ Malaysian | categories_ Massage | categories_ Meat Shops | categories_ Mediterranean | categories_ Mexican | categories_ Middle Eastern | categories_ Mini Golf | categories_ Modern European | categories_ Mongolian | categories_ Moroccan | categories_ Museums | categories_ Music & Video | categories_ Music Venues | categories_ Musicians | categories_ Nail Salons | categories_ Nail Technicians | categories_ New Mexican Cuisine | categories_ Nightlife | categories_ Noodles | categories_ Nutritionists | categories_ Organic Stores | categories_ Outlet Stores | categories_ Pakistani | categories_ Pan Asian | categories_ Party & Event Planning | categories_ Pasta Shops | categories_ Patisserie/Cake Shop | categories_ Performing Arts | categories_ Persian/Iranian | categories_ Personal Chefs | categories_ Personal Shopping | categories_ Peruvian | categories_ Pets | categories_ Piano Bars | categories_ Pizza | categories_ Playgrounds | categories_ Poke | categories_ Polish | categories_ Pool Halls | categories_ Pop-Up Restaurants | categories_ Portuguese | categories_ Poutineries | categories_ Pretzels | categories_ Professional Services | categories_ Public Markets | categories_ Public Services & Government | categories_ Pubs | categories_ Puerto Rican | categories_ Ramen | categories_ Real Estate | categories_ Resorts | categories_ Russian | categories_ Salad | categories_ Salvadoran | categories_ Sandwiches | categories_ Scandinavian | categories_ Seafood | categories_ Shanghainese | categories_ Shaved Ice | categories_ Shaved Snow | categories_ Shopping | categories_ Singaporean | categories_ Skin Care | categories_ Smokehouse | categories_ Social Clubs | categories_ Soul Food | categories_ Soup | categories_ Southern | categories_ Spanish | categories_ Speakeasies | categories_ Specialty Food | categories_ Sporting Goods | categories_ Sports Bars | categories_ Sri Lankan | categories_ Stadiums & Arenas | categories_ Steakhouses | categories_ Street Vendors | categories_ Sushi Bars | categories_ Swimming Pools | categories_ Syrian | categories_ Szechuan | categories_ Tabletop Games | categories_ Tacos | categories_ Taiwanese | categories_ Tapas Bars | categories_ Tapas/Small Plates | categories_ Tea Rooms | categories_ Teppanyaki | categories_ Tex-Mex | categories_ Thai | categories_ Themed Cafes | categories_ Tiki Bars | categories_ Tobacco Shops | categories_ Tours | categories_ Towing | categories_ Turkish | categories_ Tuscan | categories_ Ukrainian | categories_ Vegan | categories_ Vegetarian | categories_ Venezuelan | categories_ Venues & Event Spaces | categories_ Vietnamese | categories_ Waffles | categories_ Waxing | categories_ Wedding Chapels | categories_ Wedding Planning | categories_ Whiskey Bars | categories_ Wholesale Stores | categories_ Wine & Spirits | categories_ Wine Bars | 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| categories_Public Markets | categories_Public Services & Government | categories_Ramen | categories_Russian | categories_Salad | categories_Salvadoran | categories_Sandwiches | categories_Seafood | categories_Shanghainese | categories_Shaved Ice | categories_Shaved Snow | categories_Shopping | categories_Singaporean | categories_Smokehouse | categories_Social Clubs | categories_Soul Food | categories_Soup | categories_South African | categories_Southern | categories_Spanish | categories_Specialty Food | categories_Sri Lankan | categories_Steakhouses | categories_Street Vendors | categories_Sushi Bars | categories_Szechuan | categories_Tabletop Games | categories_Tacos | categories_Taiwanese | categories_Tapas Bars | categories_Tapas/Small Plates | categories_Tea Rooms | categories_Tex-Mex | categories_Thai | categories_Tobacco Shops | categories_Towing | categories_Turkish | categories_Ukrainian | categories_Vegan | categories_Vegetarian | categories_Venezuelan | categories_Venues & Event Spaces | categories_Vietnamese | categories_Waffles | categories_Wedding Planning | categories_Whiskey Bars | categories_Wholesale Stores | categories_Wineries | categories_Wraps | |
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| 1 | 1vEQsKjTfGCcNdQ2Hhuhew | yEP9vNFq3edLldNzhm6hgQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | -1 | 0.553523 | 1.800000 | 2.000000 | 1.799679 | 1.838975 | 2.007105 | 1.777964 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 2.875000 | 86 | 0 | 1 | 34 | 9 | 12 | 166 | 16.0 | 3.000000 | 15.0 | 2.909282 | 13.563981 | 1.800000 | 2.20000 | 3.686094 | 4.500000 | 3.333333 | 1.000000 | 3.000000 | 3.933014 | 3.868171 | 3.770015 | 2.000000 | 2.500000 | 3.662669 | 4.500000 | 3.333333 | 1.00000 | 3.000000 | 3.904608 | 3.851784 | 3.744434 | 1.799679 | 2.460018 | 3.678871 | 4.571695 | 3.355656 | 1.000000 | 2.924220 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | P6ZgOVwyGlvis4m2Cra13g | uH8tTLb3Fz64GtEMWpZZcQ | --6MefnULPED_I942VcFNA | 5 | 0 | 0 | 0 | 1 | 0.990602 | 4.300000 | 4.333333 | 4.299574 | 4.349620 | 4.302949 | 4.288981 | 1 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 4.108108 | 227 | 2 | 7 | 99 | 47 | 30 | 286 | 37.0 | 4.161290 | 31.0 | 4.130733 | 31.326167 | 4.300000 | 3.75000 | 3.686094 | 3.500000 | 4.454545 | 3.666667 | 3.800000 | 3.933014 | 4.000000 | 4.000000 | 4.333333 | 3.750000 | 3.662669 | 3.000000 | 4.555556 | 3.50000 | 4.000000 | 3.904608 | 4.000000 | 4.000000 | 4.299574 | 3.724926 | 3.678871 | 3.340936 | 4.538601 | 3.626374 | 3.946442 | 3.928912 | 4.00000 | 4.000000 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | ap-_EXUS49YhyukC23p2Gw | NQhvEYuYOa5psBxEoNvq2g | --6MefnULPED_I942VcFNA | 1 | 0 | 0 | 0 | 1 | 0.968214 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 0 | 3267.0 | 43.841694 | -79.399755 | 44 | 3.0 | 29.0 | 26.0 | 30.0 | 29.0 | 27.0 | 25.0 | 29.0 | 61.0 | 63.0 | 62.0 | 69.0 | 69.0 | 68.0 | 63.0 | 3.157895 | 38.0 | 3.218750 | 32.0 | 3.218815 | 33.948759 | 0.009869 | 3.703313 | 1 | 0 | 0 | 0 | 0 | 0 | 2110 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | tKFDdiQ7rGMsdxgvIz2Sxg | zbrH2lnZgWicvwoXR0qcSg | --9e1ONYQuAa-CB_Rrw7Tw | 5 | 0 | 0 | 0 | 1 | 0.995667 | 3.726715 | 3.704594 | 3.719363 | 3.752465 | 3.722907 | 3.760889 | 1 | 530.0 | 36.123183 | -115.169190 | 1613 | 4.0 | 47.0 | 42.0 | 46.0 | 45.0 | 43.0 | 41.0 | 46.0 | 59.0 | 61.0 | 60.0 | 67.0 | 69.0 | 68.0 | 61.0 | 4.107048 | 1504.0 | 4.088816 | 1216.0 | 4.092415 | 1205.738732 | 0.009869 | 3.703313 | 59 | 0 | 0 | 5 | 0 | 1 | 22 | 0.0 | 3.703313 | 0.0 | 3.703313 | 0.000000 | 3.556721 | 3.79608 | 3.686094 | 3.777956 | 3.736355 | 3.684951 | 3.789846 | 3.933014 | 3.868171 | 3.770015 | 3.542997 | 3.763461 | 3.662669 | 3.749776 | 3.719581 | 3.66752 | 3.771654 | 3.904608 | 3.851784 | 3.744434 | 3.555679 | 3.789966 | 3.678871 | 3.770170 | 3.730780 | 3.676024 | 3.788204 | 3.928912 | 3.86728 | 3.767263 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.69 0.40 0.51 50930
1 0.75 0.91 0.83 103063
accuracy 0.74 153993
macro avg 0.72 0.66 0.67 153993
weighted avg 0.73 0.74 0.72 153993
Accuracy for Deep Learning approach: 74.20402226075211
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[20454 30476] [ 9248 93815]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
102
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 36s 93us/step - loss: 0.5886 - acc: 0.6996 - val_loss: 0.5660 - val_acc: 0.7186 Epoch 2/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5544 - acc: 0.7243 - val_loss: 0.5464 - val_acc: 0.7330 Epoch 3/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5462 - acc: 0.7305 - val_loss: 0.5435 - val_acc: 0.7344 Epoch 4/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5417 - acc: 0.7337 - val_loss: 0.5363 - val_acc: 0.7391 Epoch 5/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5386 - acc: 0.7359 - val_loss: 0.5388 - val_acc: 0.7387 Epoch 6/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5362 - acc: 0.7374 - val_loss: 0.5397 - val_acc: 0.7345 Epoch 7/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5345 - acc: 0.7390 - val_loss: 0.5341 - val_acc: 0.7415 Epoch 8/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5333 - acc: 0.7401 - val_loss: 0.5340 - val_acc: 0.7381 Epoch 9/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5320 - acc: 0.7408 - val_loss: 0.5331 - val_acc: 0.7414 Epoch 10/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5308 - acc: 0.7418 - val_loss: 0.5435 - val_acc: 0.7391 Epoch 11/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5297 - acc: 0.7423 - val_loss: 0.5311 - val_acc: 0.7448 Epoch 12/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5290 - acc: 0.7427 - val_loss: 0.5284 - val_acc: 0.7451 Epoch 13/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5276 - acc: 0.7437 - val_loss: 0.5314 - val_acc: 0.7428 Epoch 14/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5272 - acc: 0.7441 - val_loss: 0.5279 - val_acc: 0.7445 Epoch 15/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5261 - acc: 0.7453 - val_loss: 0.5284 - val_acc: 0.7460 Epoch 16/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5258 - acc: 0.7452 - val_loss: 0.5294 - val_acc: 0.7438 Epoch 17/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5249 - acc: 0.7458 - val_loss: 0.5251 - val_acc: 0.7474 Epoch 18/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5241 - acc: 0.7461 - val_loss: 0.5286 - val_acc: 0.7455 Epoch 19/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5240 - acc: 0.7461 - val_loss: 0.5365 - val_acc: 0.7435 Epoch 20/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5240 - acc: 0.7461 - val_loss: 0.5237 - val_acc: 0.7488 Epoch 21/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5230 - acc: 0.7472 - val_loss: 0.5251 - val_acc: 0.7476 Epoch 22/100 390870/390870 [==============================] - 30s 77us/step - loss: 0.5225 - acc: 0.7470 - val_loss: 0.5434 - val_acc: 0.7404 Epoch 23/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5215 - acc: 0.7472 - val_loss: 0.5368 - val_acc: 0.7448 Epoch 24/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5214 - acc: 0.7477 - val_loss: 0.5253 - val_acc: 0.7475 Epoch 25/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5204 - acc: 0.7487 - val_loss: 0.5236 - val_acc: 0.7473 Epoch 26/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5209 - acc: 0.7479 - val_loss: 0.5234 - val_acc: 0.7487 Epoch 27/100 390870/390870 [==============================] - 30s 76us/step - loss: 0.5207 - acc: 0.7476 - val_loss: 0.5228 - val_acc: 0.7478 Epoch 28/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5205 - acc: 0.7486 - val_loss: 0.5221 - val_acc: 0.7495 Epoch 29/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5198 - acc: 0.7490 - val_loss: 0.5355 - val_acc: 0.7393 Epoch 30/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5194 - acc: 0.7486 - val_loss: 0.5244 - val_acc: 0.7478 Epoch 31/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5185 - acc: 0.7496 - val_loss: 0.5238 - val_acc: 0.7475 Epoch 32/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5182 - acc: 0.7494 - val_loss: 0.5210 - val_acc: 0.7490 Epoch 33/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5181 - acc: 0.7498 - val_loss: 0.5216 - val_acc: 0.7479 Epoch 34/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5170 - acc: 0.7502 - val_loss: 0.5240 - val_acc: 0.7456 Epoch 35/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5168 - acc: 0.7507 - val_loss: 0.5366 - val_acc: 0.7351 Epoch 36/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5170 - acc: 0.7507 - val_loss: 0.5230 - val_acc: 0.7468 Epoch 37/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5165 - acc: 0.7510 - val_loss: 0.5223 - val_acc: 0.7490 Epoch 38/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5163 - acc: 0.7510 - val_loss: 0.5275 - val_acc: 0.7460 Epoch 39/100 390870/390870 [==============================] - 30s 76us/step - loss: 0.5156 - acc: 0.7512 - val_loss: 0.5281 - val_acc: 0.7455 Epoch 40/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5157 - acc: 0.7510 - val_loss: 0.5248 - val_acc: 0.7486 Epoch 41/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5159 - acc: 0.7511 - val_loss: 0.5222 - val_acc: 0.7483 Epoch 42/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5156 - acc: 0.7510 - val_loss: 0.5300 - val_acc: 0.7438 Epoch 43/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5169 - acc: 0.7499 - val_loss: 0.5327 - val_acc: 0.7407 Epoch 44/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5151 - acc: 0.7513 - val_loss: 0.5237 - val_acc: 0.7489 Epoch 45/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5148 - acc: 0.7512 - val_loss: 0.5250 - val_acc: 0.7479 Epoch 46/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5146 - acc: 0.7514 - val_loss: 0.5200 - val_acc: 0.7504 Epoch 47/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5150 - acc: 0.7520 - val_loss: 0.5191 - val_acc: 0.7482 Epoch 48/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5149 - acc: 0.7515 - val_loss: 0.5229 - val_acc: 0.7511 Epoch 49/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5143 - acc: 0.7518 - val_loss: 0.5303 - val_acc: 0.7425 Epoch 50/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5139 - acc: 0.7525 - val_loss: 0.5248 - val_acc: 0.7477 Epoch 51/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5128 - acc: 0.7530 - val_loss: 0.5198 - val_acc: 0.7497 Epoch 52/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5138 - acc: 0.7517 - val_loss: 0.5212 - val_acc: 0.7498 Epoch 53/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5125 - acc: 0.7527 - val_loss: 0.5240 - val_acc: 0.7475 Epoch 54/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5129 - acc: 0.7526 - val_loss: 0.5239 - val_acc: 0.7477 Epoch 55/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5123 - acc: 0.7532 - val_loss: 0.5188 - val_acc: 0.7519 Epoch 56/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5122 - acc: 0.7531 - val_loss: 0.5258 - val_acc: 0.7484 Epoch 57/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5117 - acc: 0.7531 - val_loss: 0.5245 - val_acc: 0.7498 Epoch 58/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5121 - acc: 0.7530 - val_loss: 0.5228 - val_acc: 0.7505 Epoch 59/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5122 - acc: 0.7530 - val_loss: 0.5191 - val_acc: 0.7513 Epoch 60/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5113 - acc: 0.7537 - val_loss: 0.5171 - val_acc: 0.7529 Epoch 61/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5114 - acc: 0.7529 - val_loss: 0.5181 - val_acc: 0.7525 Epoch 62/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5112 - acc: 0.7539 - val_loss: 0.5203 - val_acc: 0.7495 Epoch 63/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5108 - acc: 0.7538 - val_loss: 0.5390 - val_acc: 0.7358 Epoch 64/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5104 - acc: 0.7538 - val_loss: 0.5204 - val_acc: 0.7518 Epoch 65/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5107 - acc: 0.7542 - val_loss: 0.5220 - val_acc: 0.7520 Epoch 66/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5102 - acc: 0.7547 - val_loss: 0.5327 - val_acc: 0.7485 Epoch 67/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5102 - acc: 0.7541 - val_loss: 0.5219 - val_acc: 0.7509 Epoch 68/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5099 - acc: 0.7545 - val_loss: 0.5195 - val_acc: 0.7510 Epoch 69/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5120 - acc: 0.7540 - val_loss: 0.5261 - val_acc: 0.7479 Epoch 70/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5105 - acc: 0.7543 - val_loss: 0.5225 - val_acc: 0.7490 Epoch 71/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5094 - acc: 0.7546 - val_loss: 0.5209 - val_acc: 0.7513 Epoch 72/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5095 - acc: 0.7550 - val_loss: 0.5199 - val_acc: 0.7501 Epoch 73/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5091 - acc: 0.7546 - val_loss: 0.5214 - val_acc: 0.7514 Epoch 74/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5100 - acc: 0.7540 - val_loss: 0.5497 - val_acc: 0.7407 Epoch 75/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5104 - acc: 0.7537 - val_loss: 0.5188 - val_acc: 0.7525 Epoch 76/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5090 - acc: 0.7547 - val_loss: 0.5196 - val_acc: 0.7532 Epoch 77/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5088 - acc: 0.7549 - val_loss: 0.5288 - val_acc: 0.7488 Epoch 78/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5086 - acc: 0.7548 - val_loss: 0.5233 - val_acc: 0.7520 Epoch 79/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5081 - acc: 0.7555 - val_loss: 0.5286 - val_acc: 0.7513 Epoch 80/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5089 - acc: 0.7548 - val_loss: 0.5277 - val_acc: 0.7494 Epoch 81/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5077 - acc: 0.7555 - val_loss: 0.5408 - val_acc: 0.7459 Epoch 82/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5085 - acc: 0.7553 - val_loss: 0.5278 - val_acc: 0.7526 Epoch 83/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5077 - acc: 0.7557 - val_loss: 0.5353 - val_acc: 0.7459 Epoch 84/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5072 - acc: 0.7560 - val_loss: 0.5307 - val_acc: 0.7504 Epoch 85/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5080 - acc: 0.7552 - val_loss: 0.5322 - val_acc: 0.7509 Epoch 86/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5071 - acc: 0.7557 - val_loss: 0.5326 - val_acc: 0.7517 Epoch 87/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5076 - acc: 0.7553 - val_loss: 0.5277 - val_acc: 0.7530 Epoch 88/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5075 - acc: 0.7559 - val_loss: 0.5375 - val_acc: 0.7482 Epoch 89/100 390870/390870 [==============================] - 30s 76us/step - loss: 0.5072 - acc: 0.7559 - val_loss: 0.5296 - val_acc: 0.7523 Epoch 90/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5063 - acc: 0.7566 - val_loss: 0.5287 - val_acc: 0.7526 Epoch 91/100 390870/390870 [==============================] - 30s 76us/step - loss: 0.5075 - acc: 0.7563 - val_loss: 0.5611 - val_acc: 0.7467 Epoch 92/100 390870/390870 [==============================] - 29s 74us/step - loss: 0.5070 - acc: 0.7558 - val_loss: 0.5295 - val_acc: 0.7526 Epoch 93/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5061 - acc: 0.7562 - val_loss: 0.5373 - val_acc: 0.7495 Epoch 94/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5068 - acc: 0.7561 - val_loss: 0.5299 - val_acc: 0.7536 Epoch 95/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5073 - acc: 0.7557 - val_loss: 0.5322 - val_acc: 0.7515 Epoch 96/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5057 - acc: 0.7571 - val_loss: 0.5339 - val_acc: 0.7524 Epoch 97/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5060 - acc: 0.7569 - val_loss: 0.5323 - val_acc: 0.7510 Epoch 98/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5065 - acc: 0.7561 - val_loss: 0.5350 - val_acc: 0.7505 Epoch 99/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5079 - acc: 0.7552 - val_loss: 0.5403 - val_acc: 0.7478 Epoch 100/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5072 - acc: 0.7562 - val_loss: 0.5282 - val_acc: 0.7527
<keras.callbacks.History at 0x20c82b579e8>
# Save trained model
classifier.save("../models/trained_deep_neural_network_2.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 19s 35us/step [0.5097503033514922, 0.7573470681567259]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.69 0.43 0.53 50930
1 0.76 0.90 0.83 103063
accuracy 0.75 153993
macro avg 0.72 0.67 0.68 153993
weighted avg 0.74 0.75 0.73 153993
Accuracy for Deep Learning approach: 74.67871916255933
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[22043 28887] [10106 92957]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 8
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
89
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 86s 220us/step - loss: 0.5889 - acc: 0.6991 - val_loss: 0.5599 - val_acc: 0.7175 Epoch 2/100 390870/390870 [==============================] - 80s 204us/step - loss: 0.5559 - acc: 0.7238 - val_loss: 0.5437 - val_acc: 0.7385 Epoch 3/100 390870/390870 [==============================] - 42s 109us/step - loss: 0.5473 - acc: 0.7302 - val_loss: 0.5678 - val_acc: 0.7257 Epoch 4/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5418 - acc: 0.7337 - val_loss: 0.5395 - val_acc: 0.7423 Epoch 5/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5385 - acc: 0.7362 - val_loss: 0.5365 - val_acc: 0.7395 Epoch 6/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5362 - acc: 0.7380 - val_loss: 0.5420 - val_acc: 0.7350 Epoch 7/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5351 - acc: 0.7386 - val_loss: 0.5403 - val_acc: 0.7368 Epoch 8/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5332 - acc: 0.7402 - val_loss: 0.5290 - val_acc: 0.7457 Epoch 9/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5317 - acc: 0.7414 - val_loss: 0.5361 - val_acc: 0.7418 Epoch 10/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5301 - acc: 0.7423 - val_loss: 0.5308 - val_acc: 0.7427 Epoch 11/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5298 - acc: 0.7424 - val_loss: 0.5374 - val_acc: 0.7386 Epoch 12/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5291 - acc: 0.7425 - val_loss: 0.5390 - val_acc: 0.7376 Epoch 13/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5269 - acc: 0.7444 - val_loss: 0.5296 - val_acc: 0.7429 Epoch 14/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5266 - acc: 0.7443 - val_loss: 0.5293 - val_acc: 0.7448 Epoch 15/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5254 - acc: 0.7455 - val_loss: 0.5251 - val_acc: 0.7473 Epoch 16/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5255 - acc: 0.7458 - val_loss: 0.5277 - val_acc: 0.7464 Epoch 17/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5240 - acc: 0.7467 - val_loss: 0.5280 - val_acc: 0.7478 Epoch 18/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5236 - acc: 0.7464 - val_loss: 0.5277 - val_acc: 0.7440 Epoch 19/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5227 - acc: 0.7474 - val_loss: 0.5282 - val_acc: 0.7450 Epoch 20/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5222 - acc: 0.7473 - val_loss: 0.5260 - val_acc: 0.7470 Epoch 21/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5218 - acc: 0.7481 - val_loss: 0.5247 - val_acc: 0.7461 Epoch 22/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5210 - acc: 0.7483 - val_loss: 0.5341 - val_acc: 0.7410 Epoch 23/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5211 - acc: 0.7482 - val_loss: 0.5279 - val_acc: 0.7444 Epoch 24/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5204 - acc: 0.7487 - val_loss: 0.5208 - val_acc: 0.7501 Epoch 25/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5200 - acc: 0.7488 - val_loss: 0.5300 - val_acc: 0.7450 Epoch 26/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5196 - acc: 0.7489 - val_loss: 0.5303 - val_acc: 0.7449 Epoch 27/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5192 - acc: 0.7493 - val_loss: 0.5473 - val_acc: 0.7363 Epoch 28/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5191 - acc: 0.7493 - val_loss: 0.5274 - val_acc: 0.7452 Epoch 29/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5182 - acc: 0.7502 - val_loss: 0.5204 - val_acc: 0.7489 Epoch 30/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5179 - acc: 0.7502 - val_loss: 0.5221 - val_acc: 0.7478 Epoch 31/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5179 - acc: 0.7500 - val_loss: 0.5256 - val_acc: 0.7455 Epoch 32/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5172 - acc: 0.7505 - val_loss: 0.5320 - val_acc: 0.7485 Epoch 33/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5165 - acc: 0.7511 - val_loss: 0.5248 - val_acc: 0.7486 Epoch 34/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5169 - acc: 0.7506 - val_loss: 0.5360 - val_acc: 0.7463 Epoch 35/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5160 - acc: 0.7514 - val_loss: 0.5229 - val_acc: 0.7469 Epoch 36/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5165 - acc: 0.7505 - val_loss: 0.5240 - val_acc: 0.7481 Epoch 37/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5160 - acc: 0.7511 - val_loss: 0.5360 - val_acc: 0.7463 Epoch 38/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5150 - acc: 0.7519 - val_loss: 0.5299 - val_acc: 0.7511 Epoch 39/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5147 - acc: 0.7517 - val_loss: 0.5296 - val_acc: 0.7458 Epoch 40/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5153 - acc: 0.7510 - val_loss: 0.5226 - val_acc: 0.7505 Epoch 41/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5142 - acc: 0.7523 - val_loss: 0.5264 - val_acc: 0.7489 Epoch 42/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5143 - acc: 0.7515 - val_loss: 0.5521 - val_acc: 0.7451 Epoch 43/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5144 - acc: 0.7522 - val_loss: 0.5299 - val_acc: 0.7487 Epoch 44/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5143 - acc: 0.7518 - val_loss: 0.5351 - val_acc: 0.7491 Epoch 45/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5137 - acc: 0.7525 - val_loss: 0.5300 - val_acc: 0.7493 Epoch 46/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5130 - acc: 0.7529 - val_loss: 0.5297 - val_acc: 0.7479 Epoch 47/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5134 - acc: 0.7528 - val_loss: 0.5308 - val_acc: 0.7460 Epoch 48/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5125 - acc: 0.7529 - val_loss: 0.5525 - val_acc: 0.7475 Epoch 49/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5125 - acc: 0.7532 - val_loss: 0.5977 - val_acc: 0.7437 Epoch 50/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5134 - acc: 0.7524 - val_loss: 0.5662 - val_acc: 0.7491 Epoch 51/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5125 - acc: 0.7535 - val_loss: 0.5283 - val_acc: 0.7511 Epoch 52/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5118 - acc: 0.7534 - val_loss: 0.5900 - val_acc: 0.7467 Epoch 53/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5117 - acc: 0.7534 - val_loss: 0.5814 - val_acc: 0.7486 Epoch 54/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5113 - acc: 0.7539 - val_loss: 0.5490 - val_acc: 0.7513 Epoch 55/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5109 - acc: 0.7541 - val_loss: 0.6131 - val_acc: 0.7465 Epoch 56/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5114 - acc: 0.7540 - val_loss: 0.6025 - val_acc: 0.7458 Epoch 57/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5107 - acc: 0.7537 - val_loss: 0.5912 - val_acc: 0.7473 Epoch 58/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5101 - acc: 0.7542 - val_loss: 0.6068 - val_acc: 0.7470 Epoch 59/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5125 - acc: 0.7535 - val_loss: 0.5276 - val_acc: 0.7503 Epoch 60/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5103 - acc: 0.7543 - val_loss: 0.5908 - val_acc: 0.7498 Epoch 61/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5100 - acc: 0.7544 - val_loss: 0.5929 - val_acc: 0.7458 Epoch 62/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5102 - acc: 0.7544 - val_loss: 0.5844 - val_acc: 0.7492 Epoch 63/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5099 - acc: 0.7542 - val_loss: 0.5892 - val_acc: 0.7468 Epoch 64/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5092 - acc: 0.7546 - val_loss: 0.5878 - val_acc: 0.7459 Epoch 65/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5099 - acc: 0.7552 - val_loss: 0.5823 - val_acc: 0.7465 Epoch 66/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5098 - acc: 0.7549 - val_loss: 0.5856 - val_acc: 0.7455 Epoch 67/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5095 - acc: 0.7547 - val_loss: 0.5800 - val_acc: 0.7489 Epoch 68/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5095 - acc: 0.7548 - val_loss: 0.6077 - val_acc: 0.7485 Epoch 69/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5085 - acc: 0.7552 - val_loss: 0.5824 - val_acc: 0.7474 Epoch 70/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5090 - acc: 0.7548 - val_loss: 0.5980 - val_acc: 0.7468 Epoch 71/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5091 - acc: 0.7549 - val_loss: 0.5952 - val_acc: 0.7473 Epoch 72/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5081 - acc: 0.7557 - val_loss: 0.6037 - val_acc: 0.7467 Epoch 73/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5097 - acc: 0.7551 - val_loss: 0.6016 - val_acc: 0.7459 Epoch 74/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5102 - acc: 0.7550 - val_loss: 0.5877 - val_acc: 0.7470 Epoch 75/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5084 - acc: 0.7553 - val_loss: 0.6011 - val_acc: 0.7427 Epoch 76/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5086 - acc: 0.7553 - val_loss: 0.5909 - val_acc: 0.7465 Epoch 77/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5080 - acc: 0.7553 - val_loss: 0.5588 - val_acc: 0.7485 Epoch 78/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5075 - acc: 0.7563 - val_loss: 0.5866 - val_acc: 0.7496 Epoch 79/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5079 - acc: 0.7554 - val_loss: 0.6126 - val_acc: 0.7452 Epoch 80/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5079 - acc: 0.7562 - val_loss: 0.5867 - val_acc: 0.7461 Epoch 81/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5076 - acc: 0.7558 - val_loss: 0.5865 - val_acc: 0.7499 Epoch 82/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5071 - acc: 0.7559 - val_loss: 0.5533 - val_acc: 0.7494 Epoch 83/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5078 - acc: 0.7559 - val_loss: 0.6015 - val_acc: 0.7485 Epoch 84/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5075 - acc: 0.7561 - val_loss: 0.5866 - val_acc: 0.7458 Epoch 85/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5075 - acc: 0.7557 - val_loss: 0.5920 - val_acc: 0.7487 Epoch 86/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5074 - acc: 0.7558 - val_loss: 0.5795 - val_acc: 0.7513 Epoch 87/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5064 - acc: 0.7567 - val_loss: 0.5217 - val_acc: 0.7531 Epoch 88/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5067 - acc: 0.7561 - val_loss: 0.5314 - val_acc: 0.7516 Epoch 89/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5061 - acc: 0.7565 - val_loss: 0.5377 - val_acc: 0.7501 Epoch 90/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5064 - acc: 0.7569 - val_loss: 0.5567 - val_acc: 0.7504 Epoch 91/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5060 - acc: 0.7568 - val_loss: 0.5890 - val_acc: 0.7495 Epoch 92/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5062 - acc: 0.7565 - val_loss: 0.5978 - val_acc: 0.7496 Epoch 93/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5063 - acc: 0.7569 - val_loss: 0.5849 - val_acc: 0.7479 Epoch 94/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5066 - acc: 0.7567 - val_loss: 0.5656 - val_acc: 0.7522 Epoch 95/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5055 - acc: 0.7570 - val_loss: 0.5727 - val_acc: 0.7496 Epoch 96/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5065 - acc: 0.7563 - val_loss: 0.5764 - val_acc: 0.7490 Epoch 97/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5064 - acc: 0.7568 - val_loss: 0.5726 - val_acc: 0.7529 Epoch 98/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5057 - acc: 0.7573 - val_loss: 0.5820 - val_acc: 0.7494 Epoch 99/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5068 - acc: 0.7562 - val_loss: 0.5329 - val_acc: 0.7543 Epoch 100/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5062 - acc: 0.7571 - val_loss: 0.5933 - val_acc: 0.7459
<keras.callbacks.History at 0x209bf585a20>
# Save trained model
classifier.save("../models/trained_deep_neural_network_3.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 18s 31us/step [0.5327735302538145, 0.7514568775011509]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.75 0.31 0.44 50930
1 0.74 0.95 0.83 103063
accuracy 0.74 153993
macro avg 0.75 0.63 0.63 153993
weighted avg 0.74 0.74 0.70 153993
Accuracy for Deep Learning approach: 73.78582143344178
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[15639 35291] [ 5077 97986]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 8
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
89
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Sixth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5898 - acc: 0.6983 - val_loss: 0.5598 - val_acc: 0.7252 Epoch 2/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5545 - acc: 0.7250 - val_loss: 0.5465 - val_acc: 0.7327 Epoch 3/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5461 - acc: 0.7305 - val_loss: 0.5469 - val_acc: 0.7400 Epoch 4/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5418 - acc: 0.7335 - val_loss: 0.5371 - val_acc: 0.7366 Epoch 5/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5390 - acc: 0.7357 - val_loss: 0.5364 - val_acc: 0.7400 Epoch 6/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5367 - acc: 0.7374 - val_loss: 0.5425 - val_acc: 0.7357 Epoch 7/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5349 - acc: 0.7387 - val_loss: 0.5291 - val_acc: 0.7436 Epoch 8/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5325 - acc: 0.7404 - val_loss: 0.5406 - val_acc: 0.7351 Epoch 9/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5315 - acc: 0.7407 - val_loss: 0.5321 - val_acc: 0.7419 Epoch 10/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5297 - acc: 0.7426 - val_loss: 0.5471 - val_acc: 0.7369 Epoch 11/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5290 - acc: 0.7426 - val_loss: 0.5304 - val_acc: 0.7417 Epoch 12/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5283 - acc: 0.7427 - val_loss: 0.5318 - val_acc: 0.7437 Epoch 13/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5276 - acc: 0.7440 - val_loss: 0.5271 - val_acc: 0.7466 Epoch 14/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5264 - acc: 0.7448 - val_loss: 0.5311 - val_acc: 0.7446 Epoch 15/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5254 - acc: 0.7448 - val_loss: 0.5278 - val_acc: 0.7474 Epoch 16/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5251 - acc: 0.7452 - val_loss: 0.5250 - val_acc: 0.7486 Epoch 17/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5242 - acc: 0.7460 - val_loss: 0.5246 - val_acc: 0.7486 Epoch 18/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5229 - acc: 0.7475 - val_loss: 0.5234 - val_acc: 0.7495 Epoch 19/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5225 - acc: 0.7470 - val_loss: 0.5234 - val_acc: 0.7487 Epoch 20/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5224 - acc: 0.7473 - val_loss: 0.5548 - val_acc: 0.7346 Epoch 21/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5216 - acc: 0.7474 - val_loss: 0.5364 - val_acc: 0.7386 Epoch 22/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5214 - acc: 0.7478 - val_loss: 0.5233 - val_acc: 0.7467 Epoch 23/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5210 - acc: 0.7483 - val_loss: 0.5213 - val_acc: 0.7495 Epoch 24/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5201 - acc: 0.7489 - val_loss: 0.5240 - val_acc: 0.7486 Epoch 25/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5194 - acc: 0.7488 - val_loss: 0.5228 - val_acc: 0.7492 Epoch 26/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5182 - acc: 0.7494 - val_loss: 0.5199 - val_acc: 0.7498 Epoch 27/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5180 - acc: 0.7495 - val_loss: 0.5270 - val_acc: 0.7470 Epoch 28/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5185 - acc: 0.7491 - val_loss: 0.5269 - val_acc: 0.7466 Epoch 29/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5177 - acc: 0.7499 - val_loss: 0.5202 - val_acc: 0.7481 Epoch 30/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5168 - acc: 0.7499 - val_loss: 0.5275 - val_acc: 0.7441 Epoch 31/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5167 - acc: 0.7501 - val_loss: 0.5252 - val_acc: 0.7460 Epoch 32/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5163 - acc: 0.7510 - val_loss: 0.5395 - val_acc: 0.7435 Epoch 33/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5161 - acc: 0.7508 - val_loss: 0.5297 - val_acc: 0.7454 Epoch 34/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5165 - acc: 0.7509 - val_loss: 0.5225 - val_acc: 0.7504 Epoch 35/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5154 - acc: 0.7511 - val_loss: 0.5214 - val_acc: 0.7497 Epoch 36/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5149 - acc: 0.7516 - val_loss: 0.5183 - val_acc: 0.7519 Epoch 37/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5157 - acc: 0.7517 - val_loss: 0.5268 - val_acc: 0.7462 Epoch 38/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5141 - acc: 0.7521 - val_loss: 0.5173 - val_acc: 0.7523 Epoch 39/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5141 - acc: 0.7523 - val_loss: 0.5186 - val_acc: 0.7506 Epoch 40/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5143 - acc: 0.7517 - val_loss: 0.5218 - val_acc: 0.7516 Epoch 41/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5143 - acc: 0.7524 - val_loss: 0.5245 - val_acc: 0.7493 Epoch 42/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5132 - acc: 0.7526 - val_loss: 0.5260 - val_acc: 0.7487 Epoch 43/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5131 - acc: 0.7527 - val_loss: 0.5201 - val_acc: 0.7504 Epoch 44/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5132 - acc: 0.7531 - val_loss: 0.5354 - val_acc: 0.7408 Epoch 45/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5131 - acc: 0.7529 - val_loss: 0.5222 - val_acc: 0.7517 Epoch 46/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5124 - acc: 0.7536 - val_loss: 0.5339 - val_acc: 0.7439 Epoch 47/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5122 - acc: 0.7536 - val_loss: 0.5231 - val_acc: 0.7508 Epoch 48/100 390870/390870 [==============================] - 30s 77us/step - loss: 0.5116 - acc: 0.7534 - val_loss: 0.5285 - val_acc: 0.7508 Epoch 49/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5121 - acc: 0.7542 - val_loss: 0.5260 - val_acc: 0.7487 Epoch 50/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5114 - acc: 0.7538 - val_loss: 0.5295 - val_acc: 0.7500 Epoch 51/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5112 - acc: 0.7542 - val_loss: 0.5229 - val_acc: 0.7492 Epoch 52/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5108 - acc: 0.7535 - val_loss: 0.5436 - val_acc: 0.7470 Epoch 53/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5105 - acc: 0.7542 - val_loss: 0.5274 - val_acc: 0.7512 Epoch 54/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5107 - acc: 0.7547 - val_loss: 0.5283 - val_acc: 0.7512 Epoch 55/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5099 - acc: 0.7543 - val_loss: 0.5323 - val_acc: 0.7503 Epoch 56/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5102 - acc: 0.7545 - val_loss: 0.5280 - val_acc: 0.7497 Epoch 57/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5105 - acc: 0.7538 - val_loss: 0.5201 - val_acc: 0.7511 Epoch 58/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5096 - acc: 0.7543 - val_loss: 0.5202 - val_acc: 0.7525 Epoch 59/100 390870/390870 [==============================] - 30s 78us/step - loss: 0.5103 - acc: 0.7541 - val_loss: 0.5213 - val_acc: 0.7499 Epoch 60/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5098 - acc: 0.7546 - val_loss: 0.5211 - val_acc: 0.7524 Epoch 61/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5091 - acc: 0.7549 - val_loss: 0.5230 - val_acc: 0.7530 Epoch 62/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5095 - acc: 0.7550 - val_loss: 0.5298 - val_acc: 0.7495 Epoch 63/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5094 - acc: 0.7550 - val_loss: 0.5252 - val_acc: 0.7515 Epoch 64/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5096 - acc: 0.7550 - val_loss: 0.5241 - val_acc: 0.7513 Epoch 65/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5093 - acc: 0.7556 - val_loss: 0.5284 - val_acc: 0.7477 Epoch 66/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5084 - acc: 0.7558 - val_loss: 0.5233 - val_acc: 0.7527 Epoch 67/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5080 - acc: 0.7558 - val_loss: 0.5339 - val_acc: 0.7475 Epoch 68/100 390870/390870 [==============================] - 28s 73us/step - loss: 0.5081 - acc: 0.7553 - val_loss: 0.5362 - val_acc: 0.7507 Epoch 69/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5075 - acc: 0.7560 - val_loss: 0.5286 - val_acc: 0.7505 Epoch 70/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5081 - acc: 0.7555 - val_loss: 0.5277 - val_acc: 0.7513 Epoch 71/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5080 - acc: 0.7554 - val_loss: 0.5272 - val_acc: 0.7519 Epoch 72/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5080 - acc: 0.7558 - val_loss: 0.5238 - val_acc: 0.7527 Epoch 73/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5073 - acc: 0.7561 - val_loss: 0.5253 - val_acc: 0.7505 Epoch 74/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5075 - acc: 0.7557 - val_loss: 0.5201 - val_acc: 0.7533 Epoch 75/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5071 - acc: 0.7563 - val_loss: 0.5419 - val_acc: 0.7488 Epoch 76/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5077 - acc: 0.7563 - val_loss: 0.5243 - val_acc: 0.7527 Epoch 77/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5071 - acc: 0.7565 - val_loss: 0.5257 - val_acc: 0.7501 Epoch 78/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5071 - acc: 0.7559 - val_loss: 0.5348 - val_acc: 0.7487 Epoch 79/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5078 - acc: 0.7558 - val_loss: 0.5285 - val_acc: 0.7524 Epoch 80/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5066 - acc: 0.7567 - val_loss: 0.5261 - val_acc: 0.7530 Epoch 81/100 390870/390870 [==============================] - 28s 70us/step - loss: 0.5072 - acc: 0.7567 - val_loss: 0.5288 - val_acc: 0.7513 Epoch 82/100 390870/390870 [==============================] - 28s 70us/step - loss: 0.5060 - acc: 0.7572 - val_loss: 0.5311 - val_acc: 0.7516 Epoch 83/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5064 - acc: 0.7565 - val_loss: 0.5298 - val_acc: 0.7528 Epoch 84/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5066 - acc: 0.7563 - val_loss: 0.5271 - val_acc: 0.7517 Epoch 85/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5062 - acc: 0.7566 - val_loss: 0.5382 - val_acc: 0.7484 Epoch 86/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5065 - acc: 0.7566 - val_loss: 0.5254 - val_acc: 0.7529 Epoch 87/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5064 - acc: 0.7570 - val_loss: 0.5450 - val_acc: 0.7492 Epoch 88/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5056 - acc: 0.7574 - val_loss: 0.5294 - val_acc: 0.7522 Epoch 89/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5057 - acc: 0.7574 - val_loss: 0.5288 - val_acc: 0.7514 Epoch 90/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5058 - acc: 0.7570 - val_loss: 0.5240 - val_acc: 0.7525 Epoch 91/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5058 - acc: 0.7568 - val_loss: 0.5285 - val_acc: 0.7534 Epoch 92/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5055 - acc: 0.7573 - val_loss: 0.5356 - val_acc: 0.7483 Epoch 93/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5050 - acc: 0.7571 - val_loss: 0.5573 - val_acc: 0.7509 Epoch 94/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5052 - acc: 0.7575 - val_loss: 0.5331 - val_acc: 0.7510 Epoch 95/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5054 - acc: 0.7574 - val_loss: 0.5303 - val_acc: 0.7522 Epoch 96/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5050 - acc: 0.7578 - val_loss: 0.5299 - val_acc: 0.7520 Epoch 97/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5047 - acc: 0.7578 - val_loss: 0.5341 - val_acc: 0.7511 Epoch 98/100 390870/390870 [==============================] - 29s 73us/step - loss: 0.5051 - acc: 0.7577 - val_loss: 0.5604 - val_acc: 0.7486 Epoch 99/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5057 - acc: 0.7576 - val_loss: 0.5413 - val_acc: 0.7446 Epoch 100/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5045 - acc: 0.7576 - val_loss: 0.5278 - val_acc: 0.7536
<keras.callbacks.History at 0x209e032a048>
# Save trained model
classifier.save("../models/trained_deep_neural_network_4.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 20s 35us/step [0.5089105000514885, 0.758450247677916]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.70 0.41 0.52 50930
1 0.76 0.91 0.83 103063
accuracy 0.75 153993
macro avg 0.73 0.66 0.67 153993
weighted avg 0.74 0.75 0.72 153993
Accuracy for Deep Learning approach: 74.58455903839784
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[20848 30082] [ 9056 94007]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
best_model = _jl.load("../models/best_SVM.joblib")
best_model.set_params(verbose=10)
best_model.get_params()
{'C': 0.001,
'class_weight': None,
'dual': True,
'fit_intercept': True,
'intercept_scaling': 1,
'loss': 'squared_hinge',
'max_iter': 50000,
'multi_class': 'ovr',
'penalty': 'l2',
'random_state': 0,
'tol': 0.0001,
'verbose': 10}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[LibLinear]
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=50000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=10)
_jl.dump(best_model, "../models/best_SVM_all.joblib")
['../models/best_SVM_all.joblib']
print("coef:", best_model.coef_)
print("intercept:", best_model.intercept_)
coef: [[-0.14394614 -0.20443318 0.34380749 ... 0.00380986 0.00857759 0. ]] intercept: [-0.31154943]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
predictions: [1 0 1 ... 0 0 0]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Support Vector Machine:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Support Vector Machine:", _accuracy_score(test_set['likes'], predic)*100)
Report for Support Vector Machine:
precision recall f1-score support
0 0.72 0.34 0.46 50930
1 0.74 0.93 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.64 0.64 153993
weighted avg 0.73 0.74 0.71 153993
Accuracy for Support Vector Machine: 73.73971544161098
# Confusion matrix for SVC
print("Confusion Matrix for SVC: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for SVC:
array([[17293, 33637],
[ 6802, 96261]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("SVM ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
params = _jl.load("../models/best_Random_Forest_2.joblib").get_params()
params['n_jobs'] = -1
params['verbose'] = 10
best_model = _RandomForestClassifier(**params)
best_model.get_params()
{'bootstrap': False,
'class_weight': None,
'criterion': 'entropy',
'max_depth': 50,
'max_features': 'auto',
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_impurity_split': None,
'min_samples_leaf': 2,
'min_samples_split': 10,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 1000,
'n_jobs': -1,
'oob_score': False,
'random_state': None,
'verbose': 10,
'warm_start': False}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.
building tree 1 of 1000 building tree 2 of 1000 building tree 3 of 1000 building tree 4 of 1000 building tree 5 of 1000 building tree 6 of 1000 building tree 7 of 1000 building tree 8 of 1000 building tree 9 of 1000 building tree 10 of 1000 building tree 11 of 1000 building tree 12 of 1000
[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 44.0s
building tree 13 of 1000 building tree 14 of 1000 building tree 15 of 1000 building tree 16 of 1000 building tree 17 of 1000 building tree 18 of 1000 building tree 19 of 1000
[Parallel(n_jobs=-1)]: Done 8 tasks | elapsed: 53.2s
building tree 20 of 1000 building tree 21 of 1000 building tree 22 of 1000 building tree 23 of 1000 building tree 24 of 1000 building tree 25 of 1000 building tree 26 of 1000 building tree 27 of 1000 building tree 28 of 1000
[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 1.9min
building tree 29 of 1000 building tree 30 of 1000 building tree 31 of 1000 building tree 32 of 1000 building tree 33 of 1000 building tree 34 of 1000 building tree 35 of 1000 building tree 36 of 1000 building tree 37 of 1000
[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 2.7min
building tree 38 of 1000 building tree 39 of 1000 building tree 40 of 1000 building tree 41 of 1000 building tree 42 of 1000 building tree 43 of 1000 building tree 44 of 1000 building tree 45 of 1000 building tree 46 of 1000 building tree 47 of 1000 building tree 48 of 1000
[Parallel(n_jobs=-1)]: Done 37 tasks | elapsed: 3.4min
building tree 49 of 1000 building tree 50 of 1000 building tree 51 of 1000 building tree 52 of 1000 building tree 53 of 1000 building tree 54 of 1000 building tree 55 of 1000 building tree 56 of 1000 building tree 57 of 1000 building tree 58 of 1000 building tree 59 of 1000
[Parallel(n_jobs=-1)]: Done 48 tasks | elapsed: 4.0min
building tree 60 of 1000 building tree 61 of 1000 building tree 62 of 1000 building tree 63 of 1000 building tree 64 of 1000 building tree 65 of 1000 building tree 66 of 1000 building tree 67 of 1000 building tree 68 of 1000 building tree 69 of 1000 building tree 70 of 1000 building tree 71 of 1000 building tree 72 of 1000
[Parallel(n_jobs=-1)]: Done 61 tasks | elapsed: 5.2min
building tree 73 of 1000 building tree 74 of 1000 building tree 75 of 1000 building tree 76 of 1000 building tree 77 of 1000 building tree 78 of 1000 building tree 79 of 1000 building tree 80 of 1000 building tree 81 of 1000 building tree 82 of 1000 building tree 83 of 1000 building tree 84 of 1000 building tree 85 of 1000
[Parallel(n_jobs=-1)]: Done 74 tasks | elapsed: 6.1min
building tree 86 of 1000 building tree 87 of 1000 building tree 88 of 1000 building tree 89 of 1000 building tree 90 of 1000 building tree 91 of 1000 building tree 92 of 1000 building tree 93 of 1000 building tree 94 of 1000 building tree 95 of 1000 building tree 96 of 1000 building tree 97 of 1000 building tree 98 of 1000 building tree 99 of 1000 building tree 100 of 1000
[Parallel(n_jobs=-1)]: Done 89 tasks | elapsed: 7.1min
building tree 101 of 1000 building tree 102 of 1000 building tree 103 of 1000 building tree 104 of 1000 building tree 105 of 1000 building tree 106 of 1000 building tree 107 of 1000 building tree 108 of 1000 building tree 109 of 1000 building tree 110 of 1000 building tree 111 of 1000 building tree 112 of 1000 building tree 113 of 1000 building tree 114 of 1000 building tree 115 of 1000
[Parallel(n_jobs=-1)]: Done 104 tasks | elapsed: 8.1min
building tree 116 of 1000 building tree 117 of 1000 building tree 118 of 1000 building tree 119 of 1000 building tree 120 of 1000 building tree 121 of 1000 building tree 122 of 1000 building tree 123 of 1000 building tree 124 of 1000 building tree 125 of 1000 building tree 126 of 1000 building tree 127 of 1000 building tree 128 of 1000 building tree 129 of 1000 building tree 130 of 1000 building tree 131 of 1000 building tree 132 of 1000
[Parallel(n_jobs=-1)]: Done 121 tasks | elapsed: 9.5min
building tree 133 of 1000 building tree 134 of 1000 building tree 135 of 1000 building tree 136 of 1000 building tree 137 of 1000 building tree 138 of 1000 building tree 139 of 1000 building tree 140 of 1000 building tree 141 of 1000 building tree 142 of 1000 building tree 143 of 1000 building tree 144 of 1000 building tree 145 of 1000 building tree 146 of 1000 building tree 147 of 1000 building tree 148 of 1000 building tree 149 of 1000
[Parallel(n_jobs=-1)]: Done 138 tasks | elapsed: 10.7min
building tree 150 of 1000 building tree 151 of 1000 building tree 152 of 1000 building tree 153 of 1000 building tree 154 of 1000 building tree 155 of 1000 building tree 156 of 1000 building tree 157 of 1000 building tree 158 of 1000 building tree 159 of 1000 building tree 160 of 1000 building tree 161 of 1000 building tree 162 of 1000 building tree 163 of 1000 building tree 164 of 1000 building tree 165 of 1000 building tree 166 of 1000 building tree 167 of 1000 building tree 168 of 1000
[Parallel(n_jobs=-1)]: Done 157 tasks | elapsed: 12.2min
building tree 169 of 1000 building tree 170 of 1000 building tree 171 of 1000 building tree 172 of 1000 building tree 173 of 1000 building tree 174 of 1000 building tree 175 of 1000 building tree 176 of 1000 building tree 177 of 1000 building tree 178 of 1000 building tree 179 of 1000 building tree 180 of 1000 building tree 181 of 1000 building tree 182 of 1000 building tree 183 of 1000 building tree 184 of 1000 building tree 185 of 1000 building tree 186 of 1000 building tree 187 of 1000
[Parallel(n_jobs=-1)]: Done 176 tasks | elapsed: 13.3min
building tree 188 of 1000 building tree 189 of 1000 building tree 190 of 1000 building tree 191 of 1000 building tree 192 of 1000 building tree 193 of 1000 building tree 194 of 1000 building tree 195 of 1000 building tree 196 of 1000 building tree 197 of 1000 building tree 198 of 1000 building tree 199 of 1000 building tree 200 of 1000 building tree 201 of 1000 building tree 202 of 1000 building tree 203 of 1000 building tree 204 of 1000 building tree 205 of 1000 building tree 206 of 1000 building tree 207 of 1000 building tree 208 of 1000 building tree 209 of 1000
[Parallel(n_jobs=-1)]: Done 197 tasks | elapsed: 14.8min
building tree 210 of 1000 building tree 211 of 1000 building tree 212 of 1000 building tree 213 of 1000 building tree 214 of 1000 building tree 215 of 1000 building tree 216 of 1000 building tree 217 of 1000 building tree 218 of 1000 building tree 219 of 1000 building tree 220 of 1000 building tree 221 of 1000 building tree 222 of 1000 building tree 223 of 1000 building tree 224 of 1000 building tree 225 of 1000 building tree 226 of 1000 building tree 227 of 1000 building tree 228 of 1000 building tree 229 of 1000
[Parallel(n_jobs=-1)]: Done 218 tasks | elapsed: 16.4min
building tree 230 of 1000 building tree 231 of 1000 building tree 232 of 1000 building tree 233 of 1000 building tree 234 of 1000 building tree 235 of 1000 building tree 236 of 1000 building tree 237 of 1000 building tree 238 of 1000 building tree 239 of 1000 building tree 240 of 1000 building tree 241 of 1000 building tree 242 of 1000 building tree 243 of 1000 building tree 244 of 1000 building tree 245 of 1000 building tree 246 of 1000 building tree 247 of 1000 building tree 248 of 1000 building tree 249 of 1000 building tree 250 of 1000 building tree 251 of 1000 building tree 252 of 1000
[Parallel(n_jobs=-1)]: Done 241 tasks | elapsed: 17.9min
building tree 253 of 1000 building tree 254 of 1000 building tree 255 of 1000 building tree 256 of 1000 building tree 257 of 1000 building tree 258 of 1000 building tree 259 of 1000 building tree 260 of 1000 building tree 261 of 1000 building tree 262 of 1000 building tree 263 of 1000 building tree 264 of 1000 building tree 265 of 1000 building tree 266 of 1000 building tree 267 of 1000 building tree 268 of 1000 building tree 269 of 1000 building tree 270 of 1000 building tree 271 of 1000 building tree 272 of 1000 building tree 273 of 1000 building tree 274 of 1000 building tree 275 of 1000
[Parallel(n_jobs=-1)]: Done 264 tasks | elapsed: 19.6min
building tree 276 of 1000 building tree 277 of 1000 building tree 278 of 1000 building tree 279 of 1000 building tree 280 of 1000 building tree 281 of 1000 building tree 282 of 1000 building tree 283 of 1000 building tree 284 of 1000 building tree 285 of 1000 building tree 286 of 1000 building tree 287 of 1000 building tree 288 of 1000 building tree 289 of 1000 building tree 290 of 1000 building tree 291 of 1000 building tree 292 of 1000 building tree 293 of 1000 building tree 294 of 1000 building tree 295 of 1000 building tree 296 of 1000 building tree 297 of 1000 building tree 298 of 1000 building tree 299 of 1000 building tree 300 of 1000
[Parallel(n_jobs=-1)]: Done 289 tasks | elapsed: 21.5min
building tree 301 of 1000 building tree 302 of 1000 building tree 303 of 1000 building tree 304 of 1000 building tree 305 of 1000 building tree 306 of 1000 building tree 307 of 1000 building tree 308 of 1000 building tree 309 of 1000 building tree 310 of 1000 building tree 311 of 1000 building tree 312 of 1000 building tree 313 of 1000 building tree 314 of 1000 building tree 315 of 1000 building tree 316 of 1000 building tree 317 of 1000 building tree 318 of 1000 building tree 319 of 1000 building tree 320 of 1000 building tree 321 of 1000 building tree 322 of 1000 building tree 323 of 1000 building tree 324 of 1000 building tree 325 of 1000
[Parallel(n_jobs=-1)]: Done 314 tasks | elapsed: 23.2min
building tree 326 of 1000 building tree 327 of 1000 building tree 328 of 1000 building tree 329 of 1000 building tree 330 of 1000 building tree 331 of 1000 building tree 332 of 1000 building tree 333 of 1000 building tree 334 of 1000 building tree 335 of 1000 building tree 336 of 1000 building tree 337 of 1000 building tree 338 of 1000 building tree 339 of 1000 building tree 340 of 1000 building tree 341 of 1000 building tree 342 of 1000 building tree 343 of 1000 building tree 344 of 1000 building tree 345 of 1000 building tree 346 of 1000 building tree 347 of 1000 building tree 348 of 1000 building tree 349 of 1000 building tree 350 of 1000 building tree 351 of 1000 building tree 352 of 1000
[Parallel(n_jobs=-1)]: Done 341 tasks | elapsed: 25.2min
building tree 353 of 1000 building tree 354 of 1000 building tree 355 of 1000 building tree 356 of 1000 building tree 357 of 1000 building tree 358 of 1000 building tree 359 of 1000 building tree 360 of 1000 building tree 361 of 1000 building tree 362 of 1000 building tree 363 of 1000 building tree 364 of 1000 building tree 365 of 1000 building tree 366 of 1000 building tree 367 of 1000 building tree 368 of 1000 building tree 369 of 1000 building tree 370 of 1000 building tree 371 of 1000 building tree 372 of 1000 building tree 373 of 1000 building tree 374 of 1000 building tree 375 of 1000 building tree 376 of 1000 building tree 377 of 1000 building tree 378 of 1000 building tree 379 of 1000
[Parallel(n_jobs=-1)]: Done 368 tasks | elapsed: 27.0min
building tree 380 of 1000 building tree 381 of 1000 building tree 382 of 1000 building tree 383 of 1000 building tree 384 of 1000 building tree 385 of 1000 building tree 386 of 1000 building tree 387 of 1000 building tree 388 of 1000 building tree 389 of 1000 building tree 390 of 1000 building tree 391 of 1000 building tree 392 of 1000 building tree 393 of 1000 building tree 394 of 1000 building tree 395 of 1000 building tree 396 of 1000 building tree 397 of 1000 building tree 398 of 1000 building tree 399 of 1000 building tree 400 of 1000 building tree 401 of 1000 building tree 402 of 1000 building tree 403 of 1000 building tree 404 of 1000 building tree 405 of 1000 building tree 406 of 1000 building tree 407 of 1000 building tree 408 of 1000
[Parallel(n_jobs=-1)]: Done 397 tasks | elapsed: 29.2min
building tree 409 of 1000 building tree 410 of 1000 building tree 411 of 1000 building tree 412 of 1000 building tree 413 of 1000 building tree 414 of 1000 building tree 415 of 1000 building tree 416 of 1000 building tree 417 of 1000 building tree 418 of 1000 building tree 419 of 1000 building tree 420 of 1000 building tree 421 of 1000 building tree 422 of 1000 building tree 423 of 1000 building tree 424 of 1000 building tree 425 of 1000 building tree 426 of 1000 building tree 427 of 1000 building tree 428 of 1000 building tree 429 of 1000 building tree 430 of 1000 building tree 431 of 1000 building tree 432 of 1000 building tree 433 of 1000 building tree 434 of 1000 building tree 435 of 1000 building tree 436 of 1000 building tree 437 of 1000
[Parallel(n_jobs=-1)]: Done 426 tasks | elapsed: 31.3min
building tree 438 of 1000 building tree 439 of 1000 building tree 440 of 1000 building tree 441 of 1000 building tree 442 of 1000 building tree 443 of 1000 building tree 444 of 1000 building tree 445 of 1000 building tree 446 of 1000 building tree 447 of 1000 building tree 448 of 1000 building tree 449 of 1000 building tree 450 of 1000 building tree 451 of 1000 building tree 452 of 1000 building tree 453 of 1000 building tree 454 of 1000 building tree 455 of 1000 building tree 456 of 1000 building tree 457 of 1000 building tree 458 of 1000 building tree 459 of 1000 building tree 460 of 1000 building tree 461 of 1000 building tree 462 of 1000 building tree 463 of 1000 building tree 464 of 1000 building tree 465 of 1000 building tree 466 of 1000 building tree 467 of 1000 building tree 468 of 1000 building tree 469 of 1000
[Parallel(n_jobs=-1)]: Done 457 tasks | elapsed: 33.5min
building tree 470 of 1000 building tree 471 of 1000 building tree 472 of 1000 building tree 473 of 1000 building tree 474 of 1000 building tree 475 of 1000 building tree 476 of 1000 building tree 477 of 1000 building tree 478 of 1000 building tree 479 of 1000 building tree 480 of 1000 building tree 481 of 1000 building tree 482 of 1000 building tree 483 of 1000 building tree 484 of 1000 building tree 485 of 1000 building tree 486 of 1000 building tree 487 of 1000 building tree 488 of 1000 building tree 489 of 1000 building tree 490 of 1000 building tree 491 of 1000 building tree 492 of 1000 building tree 493 of 1000 building tree 494 of 1000 building tree 495 of 1000 building tree 496 of 1000 building tree 497 of 1000 building tree 498 of 1000 building tree 499 of 1000
[Parallel(n_jobs=-1)]: Done 488 tasks | elapsed: 35.6min
building tree 500 of 1000 building tree 501 of 1000 building tree 502 of 1000 building tree 503 of 1000 building tree 504 of 1000 building tree 505 of 1000 building tree 506 of 1000 building tree 507 of 1000 building tree 508 of 1000 building tree 509 of 1000 building tree 510 of 1000 building tree 511 of 1000 building tree 512 of 1000 building tree 513 of 1000 building tree 514 of 1000 building tree 515 of 1000 building tree 516 of 1000 building tree 517 of 1000 building tree 518 of 1000 building tree 519 of 1000 building tree 520 of 1000 building tree 521 of 1000 building tree 522 of 1000 building tree 523 of 1000building tree 524 of 1000 building tree 525 of 1000 building tree 526 of 1000 building tree 527 of 1000 building tree 528 of 1000 building tree 529 of 1000 building tree 530 of 1000 building tree 531 of 1000 building tree 532 of 1000 building tree 533 of 1000
[Parallel(n_jobs=-1)]: Done 521 tasks | elapsed: 38.1min
building tree 534 of 1000 building tree 535 of 1000 building tree 536 of 1000 building tree 537 of 1000 building tree 538 of 1000 building tree 539 of 1000 building tree 540 of 1000 building tree 541 of 1000 building tree 542 of 1000 building tree 543 of 1000 building tree 544 of 1000 building tree 545 of 1000 building tree 546 of 1000 building tree 547 of 1000 building tree 548 of 1000 building tree 549 of 1000 building tree 550 of 1000 building tree 551 of 1000 building tree 552 of 1000 building tree 553 of 1000 building tree 554 of 1000 building tree 555 of 1000 building tree 556 of 1000 building tree 557 of 1000 building tree 558 of 1000 building tree 559 of 1000 building tree 560 of 1000 building tree 561 of 1000 building tree 562 of 1000 building tree 563 of 1000 building tree 564 of 1000 building tree 565 of 1000
[Parallel(n_jobs=-1)]: Done 554 tasks | elapsed: 40.4min
building tree 566 of 1000 building tree 567 of 1000 building tree 568 of 1000 building tree 569 of 1000 building tree 570 of 1000 building tree 571 of 1000 building tree 572 of 1000 building tree 573 of 1000 building tree 574 of 1000 building tree 575 of 1000 building tree 576 of 1000 building tree 577 of 1000 building tree 578 of 1000 building tree 579 of 1000 building tree 580 of 1000 building tree 581 of 1000 building tree 582 of 1000 building tree 583 of 1000 building tree 584 of 1000 building tree 585 of 1000 building tree 586 of 1000 building tree 587 of 1000 building tree 588 of 1000 building tree 589 of 1000 building tree 590 of 1000 building tree 591 of 1000 building tree 592 of 1000 building tree 593 of 1000 building tree 594 of 1000 building tree 595 of 1000 building tree 596 of 1000 building tree 597 of 1000 building tree 598 of 1000 building tree 599 of 1000 building tree 600 of 1000
[Parallel(n_jobs=-1)]: Done 589 tasks | elapsed: 43.1min
building tree 601 of 1000 building tree 602 of 1000 building tree 603 of 1000 building tree 604 of 1000 building tree 605 of 1000 building tree 606 of 1000 building tree 607 of 1000 building tree 608 of 1000 building tree 609 of 1000 building tree 610 of 1000 building tree 611 of 1000 building tree 612 of 1000 building tree 613 of 1000 building tree 614 of 1000 building tree 615 of 1000 building tree 616 of 1000 building tree 617 of 1000 building tree 618 of 1000 building tree 619 of 1000 building tree 620 of 1000 building tree 621 of 1000 building tree 622 of 1000 building tree 623 of 1000 building tree 624 of 1000 building tree 625 of 1000 building tree 626 of 1000 building tree 627 of 1000 building tree 628 of 1000 building tree 629 of 1000 building tree 630 of 1000 building tree 631 of 1000building tree 632 of 1000 building tree 633 of 1000 building tree 634 of 1000 building tree 635 of 1000
[Parallel(n_jobs=-1)]: Done 624 tasks | elapsed: 45.4min
building tree 636 of 1000 building tree 637 of 1000 building tree 638 of 1000 building tree 639 of 1000 building tree 640 of 1000 building tree 641 of 1000 building tree 642 of 1000 building tree 643 of 1000 building tree 644 of 1000 building tree 645 of 1000 building tree 646 of 1000 building tree 647 of 1000 building tree 648 of 1000 building tree 649 of 1000 building tree 650 of 1000 building tree 651 of 1000 building tree 652 of 1000 building tree 653 of 1000 building tree 654 of 1000 building tree 655 of 1000 building tree 656 of 1000 building tree 657 of 1000 building tree 658 of 1000 building tree 659 of 1000 building tree 660 of 1000 building tree 661 of 1000 building tree 662 of 1000 building tree 663 of 1000 building tree 664 of 1000 building tree 665 of 1000 building tree 666 of 1000 building tree 667 of 1000 building tree 668 of 1000 building tree 669 of 1000 building tree 670 of 1000 building tree 671 of 1000 building tree 672 of 1000
[Parallel(n_jobs=-1)]: Done 661 tasks | elapsed: 48.3min
building tree 673 of 1000 building tree 674 of 1000 building tree 675 of 1000 building tree 676 of 1000 building tree 677 of 1000 building tree 678 of 1000 building tree 679 of 1000 building tree 680 of 1000 building tree 681 of 1000 building tree 682 of 1000 building tree 683 of 1000 building tree 684 of 1000 building tree 685 of 1000 building tree 686 of 1000 building tree 687 of 1000 building tree 688 of 1000 building tree 689 of 1000 building tree 690 of 1000 building tree 691 of 1000 building tree 692 of 1000 building tree 693 of 1000 building tree 694 of 1000 building tree 695 of 1000 building tree 696 of 1000 building tree 697 of 1000 building tree 698 of 1000 building tree 699 of 1000 building tree 700 of 1000 building tree 701 of 1000 building tree 702 of 1000 building tree 703 of 1000 building tree 704 of 1000 building tree 705 of 1000 building tree 706 of 1000 building tree 707 of 1000 building tree 708 of 1000 building tree 709 of 1000
[Parallel(n_jobs=-1)]: Done 698 tasks | elapsed: 50.8min
building tree 710 of 1000 building tree 711 of 1000 building tree 712 of 1000 building tree 713 of 1000 building tree 714 of 1000 building tree 715 of 1000 building tree 716 of 1000 building tree 717 of 1000 building tree 718 of 1000 building tree 719 of 1000 building tree 720 of 1000 building tree 721 of 1000 building tree 722 of 1000 building tree 723 of 1000 building tree 724 of 1000 building tree 725 of 1000 building tree 726 of 1000 building tree 727 of 1000 building tree 728 of 1000 building tree 729 of 1000 building tree 730 of 1000 building tree 731 of 1000 building tree 732 of 1000 building tree 733 of 1000 building tree 734 of 1000 building tree 735 of 1000 building tree 736 of 1000 building tree 737 of 1000 building tree 738 of 1000 building tree 739 of 1000 building tree 740 of 1000 building tree 741 of 1000 building tree 742 of 1000 building tree 743 of 1000 building tree 744 of 1000 building tree 745 of 1000 building tree 746 of 1000 building tree 747 of 1000 building tree 748 of 1000 building tree 749 of 1000
[Parallel(n_jobs=-1)]: Done 737 tasks | elapsed: 53.5min
building tree 750 of 1000 building tree 751 of 1000 building tree 752 of 1000 building tree 753 of 1000 building tree 754 of 1000 building tree 755 of 1000 building tree 756 of 1000 building tree 757 of 1000 building tree 758 of 1000 building tree 759 of 1000 building tree 760 of 1000 building tree 761 of 1000 building tree 762 of 1000 building tree 763 of 1000 building tree 764 of 1000 building tree 765 of 1000 building tree 766 of 1000 building tree 767 of 1000 building tree 768 of 1000 building tree 769 of 1000 building tree 770 of 1000 building tree 771 of 1000 building tree 772 of 1000 building tree 773 of 1000 building tree 774 of 1000 building tree 775 of 1000 building tree 776 of 1000 building tree 777 of 1000 building tree 778 of 1000 building tree 779 of 1000 building tree 780 of 1000 building tree 781 of 1000 building tree 782 of 1000 building tree 783 of 1000 building tree 784 of 1000 building tree 785 of 1000 building tree 786 of 1000 building tree 787 of 1000
[Parallel(n_jobs=-1)]: Done 776 tasks | elapsed: 56.3min
building tree 788 of 1000 building tree 789 of 1000 building tree 790 of 1000 building tree 791 of 1000 building tree 792 of 1000 building tree 793 of 1000 building tree 794 of 1000 building tree 795 of 1000 building tree 796 of 1000 building tree 797 of 1000 building tree 798 of 1000 building tree 799 of 1000 building tree 800 of 1000 building tree 801 of 1000 building tree 802 of 1000 building tree 803 of 1000 building tree 804 of 1000 building tree 805 of 1000 building tree 806 of 1000 building tree 807 of 1000 building tree 808 of 1000 building tree 809 of 1000 building tree 810 of 1000 building tree 811 of 1000 building tree 812 of 1000 building tree 813 of 1000 building tree 814 of 1000 building tree 815 of 1000 building tree 816 of 1000 building tree 817 of 1000 building tree 818 of 1000 building tree 819 of 1000 building tree 820 of 1000 building tree 821 of 1000 building tree 822 of 1000 building tree 823 of 1000 building tree 824 of 1000 building tree 825 of 1000 building tree 826 of 1000 building tree 827 of 1000 building tree 828 of 1000
[Parallel(n_jobs=-1)]: Done 817 tasks | elapsed: 59.5min
building tree 829 of 1000 building tree 830 of 1000 building tree 831 of 1000 building tree 832 of 1000 building tree 833 of 1000 building tree 834 of 1000 building tree 835 of 1000 building tree 836 of 1000 building tree 837 of 1000 building tree 838 of 1000 building tree 839 of 1000 building tree 840 of 1000 building tree 841 of 1000 building tree 842 of 1000 building tree 843 of 1000 building tree 844 of 1000 building tree 845 of 1000 building tree 846 of 1000 building tree 847 of 1000 building tree 848 of 1000 building tree 849 of 1000 building tree 850 of 1000 building tree 851 of 1000 building tree 852 of 1000 building tree 853 of 1000 building tree 854 of 1000 building tree 855 of 1000 building tree 856 of 1000 building tree 857 of 1000 building tree 858 of 1000 building tree 859 of 1000 building tree 860 of 1000 building tree 861 of 1000 building tree 862 of 1000 building tree 863 of 1000 building tree 864 of 1000 building tree 865 of 1000 building tree 866 of 1000 building tree 867 of 1000 building tree 868 of 1000 building tree 869 of 1000 building tree 870 of 1000
[Parallel(n_jobs=-1)]: Done 858 tasks | elapsed: 62.3min
building tree 871 of 1000 building tree 872 of 1000 building tree 873 of 1000 building tree 874 of 1000 building tree 875 of 1000 building tree 876 of 1000 building tree 877 of 1000 building tree 878 of 1000 building tree 879 of 1000 building tree 880 of 1000 building tree 881 of 1000 building tree 882 of 1000 building tree 883 of 1000 building tree 884 of 1000 building tree 885 of 1000 building tree 886 of 1000 building tree 887 of 1000 building tree 888 of 1000 building tree 889 of 1000 building tree 890 of 1000 building tree 891 of 1000 building tree 892 of 1000 building tree 893 of 1000 building tree 894 of 1000 building tree 895 of 1000 building tree 896 of 1000 building tree 897 of 1000 building tree 898 of 1000 building tree 899 of 1000 building tree 900 of 1000 building tree 901 of 1000 building tree 902 of 1000 building tree 903 of 1000 building tree 904 of 1000 building tree 905 of 1000 building tree 906 of 1000 building tree 907 of 1000 building tree 908 of 1000 building tree 909 of 1000 building tree 910 of 1000 building tree 911 of 1000 building tree 912 of 1000
[Parallel(n_jobs=-1)]: Done 901 tasks | elapsed: 65.4min
building tree 913 of 1000 building tree 914 of 1000 building tree 915 of 1000 building tree 916 of 1000 building tree 917 of 1000 building tree 918 of 1000 building tree 919 of 1000 building tree 920 of 1000 building tree 921 of 1000 building tree 922 of 1000 building tree 923 of 1000 building tree 924 of 1000 building tree 925 of 1000 building tree 926 of 1000 building tree 927 of 1000 building tree 928 of 1000 building tree 929 of 1000 building tree 930 of 1000 building tree 931 of 1000 building tree 932 of 1000 building tree 933 of 1000 building tree 934 of 1000 building tree 935 of 1000 building tree 936 of 1000 building tree 937 of 1000 building tree 938 of 1000 building tree 939 of 1000 building tree 940 of 1000 building tree 941 of 1000 building tree 942 of 1000 building tree 943 of 1000 building tree 944 of 1000 building tree 945 of 1000 building tree 946 of 1000 building tree 947 of 1000 building tree 948 of 1000 building tree 949 of 1000 building tree 950 of 1000 building tree 951 of 1000 building tree 952 of 1000 building tree 953 of 1000 building tree 954 of 1000 building tree 955 of 1000
[Parallel(n_jobs=-1)]: Done 944 tasks | elapsed: 68.6min
building tree 956 of 1000 building tree 957 of 1000 building tree 958 of 1000 building tree 959 of 1000 building tree 960 of 1000 building tree 961 of 1000 building tree 962 of 1000 building tree 963 of 1000 building tree 964 of 1000 building tree 965 of 1000 building tree 966 of 1000 building tree 967 of 1000 building tree 968 of 1000 building tree 969 of 1000 building tree 970 of 1000 building tree 971 of 1000 building tree 972 of 1000 building tree 973 of 1000 building tree 974 of 1000 building tree 975 of 1000 building tree 976 of 1000 building tree 977 of 1000 building tree 978 of 1000 building tree 979 of 1000 building tree 980 of 1000 building tree 981 of 1000 building tree 982 of 1000 building tree 983 of 1000 building tree 984 of 1000 building tree 985 of 1000 building tree 986 of 1000 building tree 987 of 1000 building tree 988 of 1000 building tree 989 of 1000 building tree 990 of 1000 building tree 991 of 1000 building tree 992 of 1000 building tree 993 of 1000 building tree 994 of 1000 building tree 995 of 1000 building tree 996 of 1000 building tree 997 of 1000 building tree 998 of 1000 building tree 999 of 1000 building tree 1000 of 1000
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 72.3min finished
RandomForestClassifier(bootstrap=False, class_weight=None, criterion='entropy',
max_depth=50, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=2, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=-1, oob_score=False, random_state=None,
verbose=10, warm_start=False)
_jl.dump(best_model, "../models/best_Random_Forest_all.joblib")
['../models/best_Random_Forest_all.joblib']
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.4s [Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 0.4s [Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 0.8s [Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 1.2s [Parallel(n_jobs=12)]: Done 37 tasks | elapsed: 1.5s [Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 1.8s [Parallel(n_jobs=12)]: Done 61 tasks | elapsed: 2.5s [Parallel(n_jobs=12)]: Done 74 tasks | elapsed: 3.0s [Parallel(n_jobs=12)]: Done 89 tasks | elapsed: 3.4s [Parallel(n_jobs=12)]: Done 104 tasks | elapsed: 3.9s [Parallel(n_jobs=12)]: Done 121 tasks | elapsed: 4.5s [Parallel(n_jobs=12)]: Done 138 tasks | elapsed: 5.0s [Parallel(n_jobs=12)]: Done 157 tasks | elapsed: 5.7s [Parallel(n_jobs=12)]: Done 176 tasks | elapsed: 6.3s [Parallel(n_jobs=12)]: Done 197 tasks | elapsed: 7.0s [Parallel(n_jobs=12)]: Done 218 tasks | elapsed: 7.7s [Parallel(n_jobs=12)]: Done 241 tasks | elapsed: 8.5s [Parallel(n_jobs=12)]: Done 264 tasks | elapsed: 9.2s [Parallel(n_jobs=12)]: Done 289 tasks | elapsed: 10.1s [Parallel(n_jobs=12)]: Done 314 tasks | elapsed: 10.9s [Parallel(n_jobs=12)]: Done 341 tasks | elapsed: 11.7s [Parallel(n_jobs=12)]: Done 368 tasks | elapsed: 12.8s [Parallel(n_jobs=12)]: Done 397 tasks | elapsed: 14.3s [Parallel(n_jobs=12)]: Done 426 tasks | elapsed: 15.7s [Parallel(n_jobs=12)]: Done 457 tasks | elapsed: 17.1s [Parallel(n_jobs=12)]: Done 488 tasks | elapsed: 18.5s [Parallel(n_jobs=12)]: Done 521 tasks | elapsed: 19.8s [Parallel(n_jobs=12)]: Done 554 tasks | elapsed: 21.1s [Parallel(n_jobs=12)]: Done 589 tasks | elapsed: 22.4s [Parallel(n_jobs=12)]: Done 624 tasks | elapsed: 23.9s [Parallel(n_jobs=12)]: Done 661 tasks | elapsed: 25.4s [Parallel(n_jobs=12)]: Done 698 tasks | elapsed: 26.9s [Parallel(n_jobs=12)]: Done 737 tasks | elapsed: 28.4s [Parallel(n_jobs=12)]: Done 776 tasks | elapsed: 30.0s [Parallel(n_jobs=12)]: Done 817 tasks | elapsed: 31.5s [Parallel(n_jobs=12)]: Done 858 tasks | elapsed: 33.0s [Parallel(n_jobs=12)]: Done 901 tasks | elapsed: 34.5s [Parallel(n_jobs=12)]: Done 944 tasks | elapsed: 36.1s [Parallel(n_jobs=12)]: Done 1000 out of 1000 | elapsed: 37.9s finished
predictions: [0 0 1 ... 0 1 0]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Random Forest classifier:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Random Forest Classifier:", _accuracy_score(test_set['likes'], predic)*100)
Report for Random Forest classifier:
precision recall f1-score support
0 0.69 0.38 0.49 50930
1 0.75 0.92 0.82 103063
accuracy 0.74 153993
macro avg 0.72 0.65 0.66 153993
weighted avg 0.73 0.74 0.71 153993
Accuracy for Random Forest Classifier: 73.88907287993611
# Confusion matrix for Random Forest
print("Confusion Matrix for Random Forest: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for Random Forest:
array([[19194, 31736],
[ 8473, 94590]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("Random Forest ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 6
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
34
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 180s 461us/step - loss: 0.5932 - acc: 0.6968 - val_loss: 0.5693 - val_acc: 0.7170 Epoch 2/100 390870/390870 [==============================] - 143s 366us/step - loss: 0.5560 - acc: 0.7226 - val_loss: 0.5513 - val_acc: 0.7321 Epoch 3/100 390870/390870 [==============================] - 154s 395us/step - loss: 0.5464 - acc: 0.7302 - val_loss: 0.5414 - val_acc: 0.7336 Epoch 4/100 390870/390870 [==============================] - 131s 336us/step - loss: 0.5406 - acc: 0.7342 - val_loss: 0.5370 - val_acc: 0.7369 Epoch 5/100 390870/390870 [==============================] - 148s 379us/step - loss: 0.5383 - acc: 0.7357 - val_loss: 0.5326 - val_acc: 0.7431 Epoch 6/100 390870/390870 [==============================] - 163s 418us/step - loss: 0.5355 - acc: 0.7381 - val_loss: 0.5380 - val_acc: 0.7398 Epoch 7/100 390870/390870 [==============================] - 165s 422us/step - loss: 0.5340 - acc: 0.7391 - val_loss: 0.5382 - val_acc: 0.7412 Epoch 8/100 390870/390870 [==============================] - 164s 421us/step - loss: 0.5336 - acc: 0.7393 - val_loss: 0.5378 - val_acc: 0.7378 Epoch 9/100 390870/390870 [==============================] - 162s 414us/step - loss: 0.5312 - acc: 0.7413 - val_loss: 0.5396 - val_acc: 0.7402 Epoch 10/100 390870/390870 [==============================] - 165s 423us/step - loss: 0.5299 - acc: 0.7419 - val_loss: 0.5340 - val_acc: 0.7426 Epoch 11/100 390870/390870 [==============================] - 148s 380us/step - loss: 0.5294 - acc: 0.7427 - val_loss: 0.5297 - val_acc: 0.7432 Epoch 12/100 390870/390870 [==============================] - 160s 410us/step - loss: 0.5273 - acc: 0.7441 - val_loss: 0.5437 - val_acc: 0.7336 Epoch 13/100 390870/390870 [==============================] - 158s 405us/step - loss: 0.5268 - acc: 0.7442 - val_loss: 0.5254 - val_acc: 0.7457 Epoch 14/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5254 - acc: 0.7456 - val_loss: 0.5336 - val_acc: 0.7410 Epoch 15/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5254 - acc: 0.7451 - val_loss: 0.5256 - val_acc: 0.7471 Epoch 16/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5242 - acc: 0.7462 - val_loss: 0.5331 - val_acc: 0.7412 Epoch 17/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5242 - acc: 0.7461 - val_loss: 0.5247 - val_acc: 0.7477 Epoch 18/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5228 - acc: 0.7475 - val_loss: 0.5269 - val_acc: 0.7450 Epoch 19/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5239 - acc: 0.7469 - val_loss: 0.5239 - val_acc: 0.7480 Epoch 20/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5225 - acc: 0.7471 - val_loss: 0.5273 - val_acc: 0.7458 Epoch 21/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5220 - acc: 0.7477 - val_loss: 0.5330 - val_acc: 0.7401 Epoch 22/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5199 - acc: 0.7493 - val_loss: 0.5249 - val_acc: 0.7474 Epoch 23/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5196 - acc: 0.7490 - val_loss: 0.5430 - val_acc: 0.7456 Epoch 24/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5185 - acc: 0.7495 - val_loss: 0.5225 - val_acc: 0.7485 Epoch 25/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5180 - acc: 0.7501 - val_loss: 0.5236 - val_acc: 0.7483 Epoch 26/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5194 - acc: 0.7494 - val_loss: 0.5216 - val_acc: 0.7493 Epoch 27/100 390870/390870 [==============================] - 68s 175us/step - loss: 0.5184 - acc: 0.7498 - val_loss: 0.5272 - val_acc: 0.7468 Epoch 28/100 390870/390870 [==============================] - 71s 182us/step - loss: 0.5172 - acc: 0.7507 - val_loss: 0.5282 - val_acc: 0.7452 Epoch 29/100 390870/390870 [==============================] - 71s 181us/step - loss: 0.5174 - acc: 0.7503 - val_loss: 0.5236 - val_acc: 0.7483 Epoch 30/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5165 - acc: 0.7512 - val_loss: 0.5249 - val_acc: 0.7474 Epoch 31/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5163 - acc: 0.7509 - val_loss: 0.5443 - val_acc: 0.7423 Epoch 32/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5157 - acc: 0.7513 - val_loss: 0.5229 - val_acc: 0.7493 Epoch 33/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5165 - acc: 0.7510 - val_loss: 0.5228 - val_acc: 0.7485 Epoch 34/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5151 - acc: 0.7518 - val_loss: 0.5211 - val_acc: 0.7493 Epoch 35/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5148 - acc: 0.7523 - val_loss: 0.5217 - val_acc: 0.7504 Epoch 36/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5142 - acc: 0.7519 - val_loss: 0.5242 - val_acc: 0.7489 Epoch 37/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5130 - acc: 0.7527 - val_loss: 0.5208 - val_acc: 0.7496 Epoch 38/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5132 - acc: 0.7533 - val_loss: 0.5242 - val_acc: 0.7489 Epoch 39/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5136 - acc: 0.7528 - val_loss: 0.5211 - val_acc: 0.7504 Epoch 40/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5131 - acc: 0.7529 - val_loss: 0.5211 - val_acc: 0.7520 Epoch 41/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5122 - acc: 0.7534 - val_loss: 0.5201 - val_acc: 0.7498 Epoch 42/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5125 - acc: 0.7531 - val_loss: 0.5371 - val_acc: 0.7479 Epoch 43/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5127 - acc: 0.7531 - val_loss: 0.5216 - val_acc: 0.7474 Epoch 44/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5124 - acc: 0.7535 - val_loss: 0.5237 - val_acc: 0.7483 Epoch 45/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5110 - acc: 0.7542 - val_loss: 0.5254 - val_acc: 0.7495 Epoch 46/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5116 - acc: 0.7541 - val_loss: 0.5196 - val_acc: 0.7490 Epoch 47/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5117 - acc: 0.7535 - val_loss: 0.5290 - val_acc: 0.7438 Epoch 48/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5112 - acc: 0.7543 - val_loss: 0.5260 - val_acc: 0.7486 Epoch 49/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5106 - acc: 0.7545 - val_loss: 0.5208 - val_acc: 0.7501 Epoch 50/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5106 - acc: 0.7540 - val_loss: 0.5226 - val_acc: 0.7493 Epoch 51/100 390870/390870 [==============================] - 63s 162us/step - loss: 0.5115 - acc: 0.7539 - val_loss: 0.5251 - val_acc: 0.7476 Epoch 52/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5101 - acc: 0.7547 - val_loss: 0.5229 - val_acc: 0.7481 Epoch 53/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5101 - acc: 0.7549 - val_loss: 0.5207 - val_acc: 0.7514 Epoch 54/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5099 - acc: 0.7551 - val_loss: 0.5192 - val_acc: 0.7514 Epoch 55/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5093 - acc: 0.7551 - val_loss: 0.5207 - val_acc: 0.7492 Epoch 56/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5095 - acc: 0.7552 - val_loss: 0.5232 - val_acc: 0.7467 Epoch 57/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5096 - acc: 0.7549 - val_loss: 0.5234 - val_acc: 0.7490 Epoch 58/100 390870/390870 [==============================] - 63s 162us/step - loss: 0.5091 - acc: 0.7550 - val_loss: 0.5194 - val_acc: 0.7495 Epoch 59/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5115 - acc: 0.7540 - val_loss: 0.5216 - val_acc: 0.7510 Epoch 60/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5085 - acc: 0.7554 - val_loss: 0.5181 - val_acc: 0.7511 Epoch 61/100 390870/390870 [==============================] - 63s 162us/step - loss: 0.5088 - acc: 0.7554 - val_loss: 0.5281 - val_acc: 0.7476 Epoch 62/100 390870/390870 [==============================] - 63s 162us/step - loss: 0.5089 - acc: 0.7552 - val_loss: 0.5264 - val_acc: 0.7459 Epoch 63/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5097 - acc: 0.7548 - val_loss: 0.5209 - val_acc: 0.7489 Epoch 64/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5105 - acc: 0.7545 - val_loss: 0.5215 - val_acc: 0.7495 Epoch 65/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5115 - acc: 0.7535 - val_loss: 0.5253 - val_acc: 0.7475 Epoch 66/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5101 - acc: 0.7554 - val_loss: 0.5272 - val_acc: 0.7430 Epoch 67/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5081 - acc: 0.7556 - val_loss: 0.5202 - val_acc: 0.7510 Epoch 68/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5076 - acc: 0.7558 - val_loss: 0.5277 - val_acc: 0.7463 Epoch 69/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5092 - acc: 0.7555 - val_loss: 0.5229 - val_acc: 0.7468 Epoch 70/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5090 - acc: 0.7557 - val_loss: 0.5229 - val_acc: 0.7491 Epoch 71/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5071 - acc: 0.7565 - val_loss: 0.5311 - val_acc: 0.7507 Epoch 72/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5076 - acc: 0.7561 - val_loss: 0.5203 - val_acc: 0.7509 Epoch 73/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5071 - acc: 0.7566 - val_loss: 0.5237 - val_acc: 0.7501 Epoch 74/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5090 - acc: 0.7554 - val_loss: 0.5276 - val_acc: 0.7463 Epoch 75/100 390870/390870 [==============================] - 63s 162us/step - loss: 0.5068 - acc: 0.7567 - val_loss: 0.5351 - val_acc: 0.7462 Epoch 76/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5062 - acc: 0.7570 - val_loss: 0.5302 - val_acc: 0.7477 Epoch 77/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5063 - acc: 0.7563 - val_loss: 0.5270 - val_acc: 0.7512 Epoch 78/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5060 - acc: 0.7569 - val_loss: 0.5287 - val_acc: 0.7438 Epoch 79/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5058 - acc: 0.7569 - val_loss: 0.5219 - val_acc: 0.7504 Epoch 80/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5059 - acc: 0.7569 - val_loss: 0.5373 - val_acc: 0.7476 Epoch 81/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5090 - acc: 0.7553 - val_loss: 0.5302 - val_acc: 0.7470 Epoch 82/100 390870/390870 [==============================] - 71s 182us/step - loss: 0.5064 - acc: 0.7566 - val_loss: 0.5293 - val_acc: 0.7498 Epoch 83/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5057 - acc: 0.7571 - val_loss: 0.5328 - val_acc: 0.7446 Epoch 84/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5052 - acc: 0.7575 - val_loss: 0.5275 - val_acc: 0.7510 Epoch 85/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5050 - acc: 0.7571 - val_loss: 0.5285 - val_acc: 0.7506 Epoch 86/100 390870/390870 [==============================] - 64s 162us/step - loss: 0.5054 - acc: 0.7573 - val_loss: 0.5313 - val_acc: 0.7499 Epoch 87/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5061 - acc: 0.7570 - val_loss: 0.5275 - val_acc: 0.7508 Epoch 88/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5086 - acc: 0.7554 - val_loss: 0.5396 - val_acc: 0.7454 Epoch 89/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5069 - acc: 0.7568 - val_loss: 0.5216 - val_acc: 0.7506 Epoch 90/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5083 - acc: 0.7551 - val_loss: 0.5246 - val_acc: 0.7483 Epoch 91/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5057 - acc: 0.7569 - val_loss: 0.5279 - val_acc: 0.7516 Epoch 92/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5062 - acc: 0.7564 - val_loss: 0.5280 - val_acc: 0.7495 Epoch 93/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5088 - acc: 0.7545 - val_loss: 0.5226 - val_acc: 0.7500 Epoch 94/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5078 - acc: 0.7561 - val_loss: 0.5331 - val_acc: 0.7493 Epoch 95/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5052 - acc: 0.7573 - val_loss: 0.5277 - val_acc: 0.7465 Epoch 96/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5046 - acc: 0.7574 - val_loss: 0.5355 - val_acc: 0.7459 Epoch 97/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5061 - acc: 0.7565 - val_loss: 0.5250 - val_acc: 0.7497 Epoch 98/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5034 - acc: 0.7584 - val_loss: 0.5233 - val_acc: 0.7513 Epoch 99/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5042 - acc: 0.7577 - val_loss: 0.5248 - val_acc: 0.7475 Epoch 100/100 390870/390870 [==============================] - 63s 161us/step - loss: 0.5036 - acc: 0.7579 - val_loss: 0.5342 - val_acc: 0.7499
<keras.callbacks.History at 0x1d81f160d30>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 57s 101us/step [0.512360177343886, 0.7563101510431093]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[0.],
[1.],
[0.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.71 0.38 0.49 50930
1 0.75 0.92 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.65 0.66 153993
weighted avg 0.74 0.74 0.72 153993
Accuracy for Deep Learning approach: 74.33649581474484
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[19270 31660] [ 7860 95203]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 2
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
67
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 76s 194us/step - loss: 0.5945 - acc: 0.6981 - val_loss: 0.5504 - val_acc: 0.7257 Epoch 2/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5560 - acc: 0.7232 - val_loss: 0.5542 - val_acc: 0.7265 Epoch 3/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5459 - acc: 0.7304 - val_loss: 0.5586 - val_acc: 0.7329 Epoch 4/100 390870/390870 [==============================] - 73s 186us/step - loss: 0.5414 - acc: 0.7336 - val_loss: 0.5431 - val_acc: 0.7366 Epoch 5/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5378 - acc: 0.7364 - val_loss: 0.5399 - val_acc: 0.7331 Epoch 6/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5355 - acc: 0.7382 - val_loss: 0.5342 - val_acc: 0.7421 Epoch 7/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5335 - acc: 0.7393 - val_loss: 0.5430 - val_acc: 0.7335 Epoch 8/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5329 - acc: 0.7400 - val_loss: 0.5299 - val_acc: 0.7448 Epoch 9/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5309 - acc: 0.7415 - val_loss: 0.5312 - val_acc: 0.7450 Epoch 10/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5290 - acc: 0.7422 - val_loss: 0.5402 - val_acc: 0.7360 Epoch 11/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5283 - acc: 0.7437 - val_loss: 0.5288 - val_acc: 0.7454 Epoch 12/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5258 - acc: 0.7447 - val_loss: 0.5303 - val_acc: 0.7431 Epoch 13/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5260 - acc: 0.7453 - val_loss: 0.5309 - val_acc: 0.7432 Epoch 14/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5247 - acc: 0.7456 - val_loss: 0.5333 - val_acc: 0.7417 Epoch 15/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5248 - acc: 0.7458 - val_loss: 0.5262 - val_acc: 0.7480 Epoch 16/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5227 - acc: 0.7471 - val_loss: 0.5845 - val_acc: 0.7252 Epoch 17/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5222 - acc: 0.7472 - val_loss: 0.5288 - val_acc: 0.7439 Epoch 18/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5222 - acc: 0.7472 - val_loss: 0.5242 - val_acc: 0.7481 Epoch 19/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5211 - acc: 0.7481 - val_loss: 0.5295 - val_acc: 0.7480 Epoch 20/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5202 - acc: 0.7478 - val_loss: 0.5281 - val_acc: 0.7466 Epoch 21/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5202 - acc: 0.7481 - val_loss: 0.5229 - val_acc: 0.7487 Epoch 22/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5186 - acc: 0.7495 - val_loss: 0.5255 - val_acc: 0.7470 Epoch 23/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5183 - acc: 0.7493 - val_loss: 0.5234 - val_acc: 0.7496 Epoch 24/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5175 - acc: 0.7502 - val_loss: 0.5376 - val_acc: 0.7437 Epoch 25/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5175 - acc: 0.7507 - val_loss: 0.5282 - val_acc: 0.7466 Epoch 26/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5170 - acc: 0.7500 - val_loss: 0.5223 - val_acc: 0.7488 Epoch 27/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5204 - acc: 0.7479 - val_loss: 0.5407 - val_acc: 0.7352 Epoch 28/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5160 - acc: 0.7510 - val_loss: 0.5215 - val_acc: 0.7495 Epoch 29/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5156 - acc: 0.7510 - val_loss: 0.5270 - val_acc: 0.7460 Epoch 30/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5145 - acc: 0.7519 - val_loss: 0.5264 - val_acc: 0.7464 Epoch 31/100 390870/390870 [==============================] - 72s 183us/step - loss: 0.5155 - acc: 0.7515 - val_loss: 0.5299 - val_acc: 0.7429 Epoch 32/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5154 - acc: 0.7516 - val_loss: 0.5260 - val_acc: 0.7458 Epoch 33/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5160 - acc: 0.7506 - val_loss: 0.5369 - val_acc: 0.7389 Epoch 34/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5151 - acc: 0.7512 - val_loss: 0.5578 - val_acc: 0.7236 Epoch 35/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5131 - acc: 0.7525 - val_loss: 0.5299 - val_acc: 0.7433 Epoch 36/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5125 - acc: 0.7526 - val_loss: 0.5241 - val_acc: 0.7469 Epoch 37/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5139 - acc: 0.7525 - val_loss: 0.5235 - val_acc: 0.7478 Epoch 38/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5135 - acc: 0.7529 - val_loss: 0.5226 - val_acc: 0.7495 Epoch 39/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5153 - acc: 0.7518 - val_loss: 0.5248 - val_acc: 0.7476 Epoch 40/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5110 - acc: 0.7542 - val_loss: 0.5360 - val_acc: 0.7444 Epoch 41/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5110 - acc: 0.7541 - val_loss: 0.5234 - val_acc: 0.7478 Epoch 42/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5109 - acc: 0.7545 - val_loss: 0.5260 - val_acc: 0.7481 Epoch 43/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5105 - acc: 0.7537 - val_loss: 0.5239 - val_acc: 0.7488 Epoch 44/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5109 - acc: 0.7540 - val_loss: 0.5303 - val_acc: 0.7405 Epoch 45/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5103 - acc: 0.7549 - val_loss: 0.5251 - val_acc: 0.7485 Epoch 46/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5092 - acc: 0.7550 - val_loss: 0.5380 - val_acc: 0.7445 Epoch 47/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5096 - acc: 0.7551 - val_loss: 0.5319 - val_acc: 0.7433 Epoch 48/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5082 - acc: 0.7555 - val_loss: 0.5193 - val_acc: 0.7508 Epoch 49/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5084 - acc: 0.7553 - val_loss: 0.5240 - val_acc: 0.7484 Epoch 50/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5092 - acc: 0.7554 - val_loss: 0.5313 - val_acc: 0.7443 Epoch 51/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5087 - acc: 0.7554 - val_loss: 0.5237 - val_acc: 0.7494 Epoch 52/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5074 - acc: 0.7562 - val_loss: 0.5261 - val_acc: 0.7464 Epoch 53/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5076 - acc: 0.7557 - val_loss: 0.5212 - val_acc: 0.7500 Epoch 54/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5068 - acc: 0.7558 - val_loss: 0.5263 - val_acc: 0.7494 Epoch 55/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5080 - acc: 0.7559 - val_loss: 0.5306 - val_acc: 0.7435 Epoch 56/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5069 - acc: 0.7564 - val_loss: 0.5259 - val_acc: 0.7492 Epoch 57/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5067 - acc: 0.7562 - val_loss: 0.5279 - val_acc: 0.7469 Epoch 58/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5069 - acc: 0.7559 - val_loss: 0.5317 - val_acc: 0.7491 Epoch 59/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5068 - acc: 0.7570 - val_loss: 0.5292 - val_acc: 0.7458 Epoch 60/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5057 - acc: 0.7573 - val_loss: 0.5221 - val_acc: 0.7499 Epoch 61/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5063 - acc: 0.7571 - val_loss: 0.5270 - val_acc: 0.7494 Epoch 62/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5055 - acc: 0.7571 - val_loss: 0.5219 - val_acc: 0.7496 Epoch 63/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5046 - acc: 0.7573 - val_loss: 0.5254 - val_acc: 0.7471 Epoch 64/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5055 - acc: 0.7571 - val_loss: 0.5264 - val_acc: 0.7494 Epoch 65/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5039 - acc: 0.7576 - val_loss: 0.5297 - val_acc: 0.7470 Epoch 66/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5045 - acc: 0.7574 - val_loss: 0.5283 - val_acc: 0.7481 Epoch 67/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5056 - acc: 0.7572 - val_loss: 0.5276 - val_acc: 0.7473 Epoch 68/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5059 - acc: 0.7566 - val_loss: 0.5221 - val_acc: 0.7494 Epoch 69/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5039 - acc: 0.7581 - val_loss: 0.5297 - val_acc: 0.7480 Epoch 70/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5033 - acc: 0.7584 - val_loss: 0.5300 - val_acc: 0.7451 Epoch 71/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5034 - acc: 0.7576 - val_loss: 0.5224 - val_acc: 0.7500 Epoch 72/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5023 - acc: 0.7587 - val_loss: 0.5275 - val_acc: 0.7484 Epoch 73/100 390870/390870 [==============================] - 71s 182us/step - loss: 0.5031 - acc: 0.7584 - val_loss: 0.5269 - val_acc: 0.7485 Epoch 74/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5027 - acc: 0.7584 - val_loss: 0.5260 - val_acc: 0.7488 Epoch 75/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5018 - acc: 0.7597 - val_loss: 0.5272 - val_acc: 0.7478 Epoch 76/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5026 - acc: 0.7590 - val_loss: 0.5292 - val_acc: 0.7493 Epoch 77/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5021 - acc: 0.7591 - val_loss: 0.5394 - val_acc: 0.7473 Epoch 78/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5029 - acc: 0.7586 - val_loss: 0.5319 - val_acc: 0.7476 Epoch 79/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5027 - acc: 0.7591 - val_loss: 0.5461 - val_acc: 0.7434 Epoch 80/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5021 - acc: 0.7592 - val_loss: 0.5273 - val_acc: 0.7498 Epoch 81/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5015 - acc: 0.7594 - val_loss: 0.5773 - val_acc: 0.7094 Epoch 82/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5009 - acc: 0.7600 - val_loss: 0.5287 - val_acc: 0.7478 Epoch 83/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5015 - acc: 0.7593 - val_loss: 0.5263 - val_acc: 0.7478 Epoch 84/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5016 - acc: 0.7588 - val_loss: 0.5305 - val_acc: 0.7472 Epoch 85/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5015 - acc: 0.7598 - val_loss: 0.5262 - val_acc: 0.7498 Epoch 86/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5003 - acc: 0.7598 - val_loss: 0.5268 - val_acc: 0.7487 Epoch 87/100 390870/390870 [==============================] - 71s 181us/step - loss: 0.5003 - acc: 0.7599 - val_loss: 0.5320 - val_acc: 0.7493 Epoch 88/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5043 - acc: 0.7576 - val_loss: 0.5263 - val_acc: 0.7477 Epoch 89/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5013 - acc: 0.7593 - val_loss: 0.5292 - val_acc: 0.7459 Epoch 90/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5003 - acc: 0.7599 - val_loss: 0.5309 - val_acc: 0.7497 Epoch 91/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5030 - acc: 0.7584 - val_loss: 0.5248 - val_acc: 0.7496 Epoch 92/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5012 - acc: 0.7596 - val_loss: 0.5330 - val_acc: 0.7478 Epoch 93/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5009 - acc: 0.7593 - val_loss: 0.5318 - val_acc: 0.7475 Epoch 94/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.4989 - acc: 0.7607 - val_loss: 0.5308 - val_acc: 0.7487 Epoch 95/100 390870/390870 [==============================] - 65s 168us/step - loss: 0.4990 - acc: 0.7607 - val_loss: 0.5308 - val_acc: 0.7497 Epoch 96/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.4990 - acc: 0.7613 - val_loss: 0.5352 - val_acc: 0.7477 Epoch 97/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.4985 - acc: 0.7607 - val_loss: 0.5291 - val_acc: 0.7488 Epoch 98/100 390870/390870 [==============================] - 65s 168us/step - loss: 0.4983 - acc: 0.7615 - val_loss: 0.5259 - val_acc: 0.7461 Epoch 99/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.4999 - acc: 0.7602 - val_loss: 0.5438 - val_acc: 0.7421 Epoch 100/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.4978 - acc: 0.7614 - val_loss: 0.5337 - val_acc: 0.7468
<keras.callbacks.History at 0x1dcde2b5d68>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all2.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 51s 90us/step [0.5102951304472344, 0.7552123441486025]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[1.],
[1.],
[1.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.73 0.33 0.46 50930
1 0.74 0.94 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.64 0.64 153993
weighted avg 0.74 0.74 0.70 153993
Accuracy for Deep Learning approach: 73.82478424343964
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[16881 34049] [ 6259 96804]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
29
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 82s 209us/step - loss: 0.5856 - acc: 0.7017 - val_loss: 0.5694 - val_acc: 0.7125 Epoch 2/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5533 - acc: 0.7251 - val_loss: 0.5492 - val_acc: 0.7293 Epoch 3/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5445 - acc: 0.7317 - val_loss: 0.5462 - val_acc: 0.7312 Epoch 4/100 390870/390870 [==============================] - 73s 187us/step - loss: 0.5410 - acc: 0.7339 - val_loss: 0.5389 - val_acc: 0.7389 Epoch 5/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5381 - acc: 0.7366 - val_loss: 0.5335 - val_acc: 0.7427 Epoch 6/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5354 - acc: 0.7376 - val_loss: 0.5365 - val_acc: 0.7390 Epoch 7/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5334 - acc: 0.7398 - val_loss: 0.5297 - val_acc: 0.7436 Epoch 8/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5324 - acc: 0.7405 - val_loss: 0.5356 - val_acc: 0.7383 Epoch 9/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5301 - acc: 0.7424 - val_loss: 0.5294 - val_acc: 0.7443 Epoch 10/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5293 - acc: 0.7425 - val_loss: 0.5266 - val_acc: 0.7462 Epoch 11/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5279 - acc: 0.7434 - val_loss: 0.5368 - val_acc: 0.7414 Epoch 12/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5273 - acc: 0.7445 - val_loss: 0.5278 - val_acc: 0.7459 Epoch 13/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5260 - acc: 0.7443 - val_loss: 0.5314 - val_acc: 0.7415 Epoch 14/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5261 - acc: 0.7449 - val_loss: 0.5342 - val_acc: 0.7440 Epoch 15/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5254 - acc: 0.7453 - val_loss: 0.5326 - val_acc: 0.7456 Epoch 16/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5241 - acc: 0.7460 - val_loss: 0.5312 - val_acc: 0.7421 Epoch 17/100 390870/390870 [==============================] - 67s 173us/step - loss: 0.5248 - acc: 0.7454 - val_loss: 0.5303 - val_acc: 0.7437 Epoch 18/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5236 - acc: 0.7460 - val_loss: 0.5257 - val_acc: 0.7455 Epoch 19/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5216 - acc: 0.7477 - val_loss: 0.5236 - val_acc: 0.7479 Epoch 20/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5218 - acc: 0.7474 - val_loss: 0.5258 - val_acc: 0.7471 Epoch 21/100 390870/390870 [==============================] - 73s 188us/step - loss: 0.5221 - acc: 0.7474 - val_loss: 0.5318 - val_acc: 0.7455 Epoch 22/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5209 - acc: 0.7478 - val_loss: 0.5259 - val_acc: 0.7465 Epoch 23/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5220 - acc: 0.7472 - val_loss: 0.5280 - val_acc: 0.7454 Epoch 24/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5215 - acc: 0.7482 - val_loss: 0.5226 - val_acc: 0.7485 Epoch 25/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5219 - acc: 0.7473 - val_loss: 0.5232 - val_acc: 0.7481 Epoch 26/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5193 - acc: 0.7486 - val_loss: 0.5268 - val_acc: 0.7462 Epoch 27/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5184 - acc: 0.7495 - val_loss: 0.5267 - val_acc: 0.7469 Epoch 28/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5189 - acc: 0.7495 - val_loss: 0.5243 - val_acc: 0.7468 Epoch 29/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5178 - acc: 0.7499 - val_loss: 0.5323 - val_acc: 0.7437 Epoch 30/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5177 - acc: 0.7498 - val_loss: 0.5235 - val_acc: 0.7464 Epoch 31/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5170 - acc: 0.7503 - val_loss: 0.5246 - val_acc: 0.7437 Epoch 32/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5164 - acc: 0.7512 - val_loss: 0.5275 - val_acc: 0.7441 Epoch 33/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5160 - acc: 0.7507 - val_loss: 0.5223 - val_acc: 0.7495 Epoch 34/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5156 - acc: 0.7514 - val_loss: 0.5273 - val_acc: 0.7480 Epoch 35/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5156 - acc: 0.7514 - val_loss: 0.5261 - val_acc: 0.7478 Epoch 36/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5147 - acc: 0.7515 - val_loss: 0.5208 - val_acc: 0.7484 Epoch 37/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5148 - acc: 0.7517 - val_loss: 0.5228 - val_acc: 0.7475 Epoch 38/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5145 - acc: 0.7511 - val_loss: 0.5331 - val_acc: 0.7420 Epoch 39/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5140 - acc: 0.7521 - val_loss: 0.5244 - val_acc: 0.7469 Epoch 40/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5134 - acc: 0.7525 - val_loss: 0.5236 - val_acc: 0.7451 Epoch 41/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5137 - acc: 0.7521 - val_loss: 0.5223 - val_acc: 0.7479 Epoch 42/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5142 - acc: 0.7516 - val_loss: 0.5231 - val_acc: 0.7477 Epoch 43/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5129 - acc: 0.7530 - val_loss: 0.5260 - val_acc: 0.7479 Epoch 44/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5140 - acc: 0.7520 - val_loss: 0.5233 - val_acc: 0.7477 Epoch 45/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5131 - acc: 0.7522 - val_loss: 0.5250 - val_acc: 0.7470 Epoch 46/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5142 - acc: 0.7520 - val_loss: 0.5305 - val_acc: 0.7462 Epoch 47/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5134 - acc: 0.7525 - val_loss: 0.5262 - val_acc: 0.7454 Epoch 48/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5118 - acc: 0.7535 - val_loss: 0.5210 - val_acc: 0.7506 Epoch 49/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5119 - acc: 0.7536 - val_loss: 0.5244 - val_acc: 0.7473 Epoch 50/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5121 - acc: 0.7537 - val_loss: 0.5229 - val_acc: 0.7498 Epoch 51/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5118 - acc: 0.7534 - val_loss: 0.5242 - val_acc: 0.7471 Epoch 52/100 390870/390870 [==============================] - 65s 165us/step - loss: 0.5109 - acc: 0.7543 - val_loss: 0.5290 - val_acc: 0.7461 Epoch 53/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5110 - acc: 0.7537 - val_loss: 0.5242 - val_acc: 0.7479 Epoch 54/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5114 - acc: 0.7533 - val_loss: 0.5283 - val_acc: 0.7450 Epoch 55/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5109 - acc: 0.7540 - val_loss: 0.5282 - val_acc: 0.7471 Epoch 56/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5105 - acc: 0.7545 - val_loss: 0.5238 - val_acc: 0.7469 Epoch 57/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5102 - acc: 0.7547 - val_loss: 0.5273 - val_acc: 0.7467 Epoch 58/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5102 - acc: 0.7545 - val_loss: 0.5283 - val_acc: 0.7459 Epoch 59/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5103 - acc: 0.7540 - val_loss: 0.5335 - val_acc: 0.7429 Epoch 60/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5104 - acc: 0.7539 - val_loss: 0.5281 - val_acc: 0.7426 Epoch 61/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5099 - acc: 0.7544 - val_loss: 0.5273 - val_acc: 0.7482 Epoch 62/100 390870/390870 [==============================] - 69s 178us/step - loss: 0.5114 - acc: 0.7540 - val_loss: 0.5351 - val_acc: 0.7420 Epoch 63/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5107 - acc: 0.7540 - val_loss: 0.5387 - val_acc: 0.7308 Epoch 64/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5099 - acc: 0.7547 - val_loss: 0.5230 - val_acc: 0.7477 Epoch 65/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5098 - acc: 0.7548 - val_loss: 0.5240 - val_acc: 0.7484 Epoch 66/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5092 - acc: 0.7552 - val_loss: 0.5368 - val_acc: 0.7424 Epoch 67/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5087 - acc: 0.7551 - val_loss: 0.5328 - val_acc: 0.7424 Epoch 68/100 390870/390870 [==============================] - 63s 162us/step - loss: 0.5097 - acc: 0.7542 - val_loss: 0.5264 - val_acc: 0.7458 Epoch 69/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5085 - acc: 0.7557 - val_loss: 0.5245 - val_acc: 0.7476 Epoch 70/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5086 - acc: 0.7556 - val_loss: 0.5275 - val_acc: 0.7456 Epoch 71/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5086 - acc: 0.7547 - val_loss: 0.5261 - val_acc: 0.7472 Epoch 72/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5085 - acc: 0.7556 - val_loss: 0.5221 - val_acc: 0.7496 Epoch 73/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5084 - acc: 0.7551 - val_loss: 0.5211 - val_acc: 0.7501 Epoch 74/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5088 - acc: 0.7554 - val_loss: 0.5263 - val_acc: 0.7473 Epoch 75/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5091 - acc: 0.7546 - val_loss: 0.5258 - val_acc: 0.7480 Epoch 76/100 390870/390870 [==============================] - 72s 185us/step - loss: 0.5073 - acc: 0.7561 - val_loss: 0.5252 - val_acc: 0.7488 Epoch 77/100 390870/390870 [==============================] - 68s 175us/step - loss: 0.5082 - acc: 0.7550 - val_loss: 0.5250 - val_acc: 0.7473 Epoch 78/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5072 - acc: 0.7559 - val_loss: 0.5376 - val_acc: 0.7452 Epoch 79/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5082 - acc: 0.7559 - val_loss: 0.5283 - val_acc: 0.7422 Epoch 80/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5081 - acc: 0.7551 - val_loss: 0.5314 - val_acc: 0.7409 Epoch 81/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5068 - acc: 0.7567 - val_loss: 0.5225 - val_acc: 0.7490 Epoch 82/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5067 - acc: 0.7562 - val_loss: 0.5258 - val_acc: 0.7491 Epoch 83/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5080 - acc: 0.7559 - val_loss: 0.5203 - val_acc: 0.7497 Epoch 84/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5062 - acc: 0.7567 - val_loss: 0.5245 - val_acc: 0.7490 Epoch 85/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5069 - acc: 0.7559 - val_loss: 0.5207 - val_acc: 0.7505 Epoch 86/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5067 - acc: 0.7566 - val_loss: 0.5253 - val_acc: 0.7466 Epoch 87/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5066 - acc: 0.7565 - val_loss: 0.5364 - val_acc: 0.7421 Epoch 88/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5062 - acc: 0.7562 - val_loss: 0.5209 - val_acc: 0.7493 Epoch 89/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5061 - acc: 0.7562 - val_loss: 0.5227 - val_acc: 0.7481 Epoch 90/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5058 - acc: 0.7566 - val_loss: 0.5244 - val_acc: 0.7472 Epoch 91/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5061 - acc: 0.7562 - val_loss: 0.5256 - val_acc: 0.7465 Epoch 92/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5057 - acc: 0.7563 - val_loss: 0.5331 - val_acc: 0.7471 Epoch 93/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5055 - acc: 0.7568 - val_loss: 0.5243 - val_acc: 0.7490 Epoch 94/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5056 - acc: 0.7565 - val_loss: 0.5261 - val_acc: 0.7478 Epoch 95/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5058 - acc: 0.7570 - val_loss: 0.5244 - val_acc: 0.7467 Epoch 96/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5049 - acc: 0.7573 - val_loss: 0.5215 - val_acc: 0.7513 Epoch 97/100 390870/390870 [==============================] - 64s 164us/step - loss: 0.5062 - acc: 0.7567 - val_loss: 0.5249 - val_acc: 0.7499 Epoch 98/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5066 - acc: 0.7565 - val_loss: 0.5254 - val_acc: 0.7505 Epoch 99/100 390870/390870 [==============================] - 64s 165us/step - loss: 0.5047 - acc: 0.7572 - val_loss: 0.5241 - val_acc: 0.7479 Epoch 100/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5046 - acc: 0.7572 - val_loss: 0.5229 - val_acc: 0.7483
<keras.callbacks.History at 0x1e2ac885a58>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all3.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 54s 96us/step [0.5092164321641403, 0.7539193317881909]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[0.],
[1.],
[0.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.70 0.38 0.49 50930
1 0.75 0.92 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.65 0.66 153993
weighted avg 0.74 0.74 0.72 153993
Accuracy for Deep Learning approach: 74.23843940958356
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[19398 31532] [ 8139 94924]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
29
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 76s 194us/step - loss: 0.5860 - acc: 0.7003 - val_loss: 0.5558 - val_acc: 0.7246 Epoch 2/100 390870/390870 [==============================] - 68s 175us/step - loss: 0.5507 - acc: 0.7276 - val_loss: 0.5472 - val_acc: 0.7286 Epoch 3/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5437 - acc: 0.7324 - val_loss: 0.5416 - val_acc: 0.7332 Epoch 4/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5389 - acc: 0.7367 - val_loss: 0.5404 - val_acc: 0.7350 Epoch 5/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5355 - acc: 0.7380 - val_loss: 0.5455 - val_acc: 0.7335 Epoch 6/100 390870/390870 [==============================] - 82s 210us/step - loss: 0.5338 - acc: 0.7389 - val_loss: 0.5305 - val_acc: 0.7453 Epoch 7/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5318 - acc: 0.7409 - val_loss: 0.5410 - val_acc: 0.7380 Epoch 8/100 390870/390870 [==============================] - 69s 178us/step - loss: 0.5310 - acc: 0.7413 - val_loss: 0.5284 - val_acc: 0.7453 Epoch 9/100 390870/390870 [==============================] - 70s 178us/step - loss: 0.5294 - acc: 0.7424 - val_loss: 0.5343 - val_acc: 0.7369 Epoch 10/100 390870/390870 [==============================] - 69s 178us/step - loss: 0.5278 - acc: 0.7440 - val_loss: 0.5295 - val_acc: 0.7431 Epoch 11/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5272 - acc: 0.7438 - val_loss: 0.5369 - val_acc: 0.7398 Epoch 12/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5273 - acc: 0.7443 - val_loss: 0.5292 - val_acc: 0.7453 Epoch 13/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5255 - acc: 0.7451 - val_loss: 0.5319 - val_acc: 0.7404 Epoch 14/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5257 - acc: 0.7452 - val_loss: 0.5338 - val_acc: 0.7419 Epoch 15/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5247 - acc: 0.7460 - val_loss: 0.5309 - val_acc: 0.7463 Epoch 16/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5230 - acc: 0.7470 - val_loss: 0.5235 - val_acc: 0.7487 Epoch 17/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5225 - acc: 0.7472 - val_loss: 0.5274 - val_acc: 0.7448 Epoch 18/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5218 - acc: 0.7477 - val_loss: 0.5264 - val_acc: 0.7470 Epoch 19/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5210 - acc: 0.7483 - val_loss: 0.5228 - val_acc: 0.7489 Epoch 20/100 390870/390870 [==============================] - 74s 189us/step - loss: 0.5203 - acc: 0.7481 - val_loss: 0.5470 - val_acc: 0.7400 Epoch 21/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5203 - acc: 0.7486 - val_loss: 0.5242 - val_acc: 0.7466 Epoch 22/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5194 - acc: 0.7491 - val_loss: 0.5245 - val_acc: 0.7475 Epoch 23/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5192 - acc: 0.7496 - val_loss: 0.5237 - val_acc: 0.7484 Epoch 24/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5187 - acc: 0.7499 - val_loss: 0.5245 - val_acc: 0.7465 Epoch 25/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5189 - acc: 0.7495 - val_loss: 0.5257 - val_acc: 0.7472 Epoch 26/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5177 - acc: 0.7501 - val_loss: 0.5225 - val_acc: 0.7483 Epoch 27/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5175 - acc: 0.7505 - val_loss: 0.5334 - val_acc: 0.7459 Epoch 28/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5164 - acc: 0.7506 - val_loss: 0.5292 - val_acc: 0.7443 Epoch 29/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5173 - acc: 0.7504 - val_loss: 0.5240 - val_acc: 0.7487 Epoch 30/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5161 - acc: 0.7515 - val_loss: 0.5209 - val_acc: 0.7504 Epoch 31/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5160 - acc: 0.7512 - val_loss: 0.5322 - val_acc: 0.7455 Epoch 32/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5155 - acc: 0.7513 - val_loss: 0.5241 - val_acc: 0.7481 Epoch 33/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5147 - acc: 0.7520 - val_loss: 0.5242 - val_acc: 0.7483 Epoch 34/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5151 - acc: 0.7522 - val_loss: 0.5218 - val_acc: 0.7489 Epoch 35/100 390870/390870 [==============================] - 68s 175us/step - loss: 0.5145 - acc: 0.7526 - val_loss: 0.5315 - val_acc: 0.7455 Epoch 36/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5145 - acc: 0.7524 - val_loss: 0.5201 - val_acc: 0.7499 Epoch 37/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5143 - acc: 0.7519 - val_loss: 0.5216 - val_acc: 0.7484 Epoch 38/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5135 - acc: 0.7530 - val_loss: 0.5245 - val_acc: 0.7464 Epoch 39/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5133 - acc: 0.7526 - val_loss: 0.5236 - val_acc: 0.7473 Epoch 40/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5127 - acc: 0.7528 - val_loss: 0.5242 - val_acc: 0.7474 Epoch 41/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5129 - acc: 0.7524 - val_loss: 0.5211 - val_acc: 0.7483 Epoch 42/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5122 - acc: 0.7535 - val_loss: 0.5401 - val_acc: 0.7440 Epoch 43/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5121 - acc: 0.7537 - val_loss: 0.5244 - val_acc: 0.7481 Epoch 44/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5139 - acc: 0.7533 - val_loss: 0.5190 - val_acc: 0.7515 Epoch 45/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5117 - acc: 0.7536 - val_loss: 0.5238 - val_acc: 0.7503 Epoch 46/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5113 - acc: 0.7544 - val_loss: 0.5190 - val_acc: 0.7514 Epoch 47/100 390870/390870 [==============================] - 74s 188us/step - loss: 0.5112 - acc: 0.7541 - val_loss: 0.5202 - val_acc: 0.7507 Epoch 48/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5108 - acc: 0.7543 - val_loss: 0.5216 - val_acc: 0.7516 Epoch 49/100 390870/390870 [==============================] - 72s 183us/step - loss: 0.5107 - acc: 0.7543 - val_loss: 0.5286 - val_acc: 0.7482 Epoch 50/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5116 - acc: 0.7535 - val_loss: 0.5198 - val_acc: 0.7505 Epoch 51/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5111 - acc: 0.7538 - val_loss: 0.5203 - val_acc: 0.7493 Epoch 52/100 390870/390870 [==============================] - 71s 182us/step - loss: 0.5107 - acc: 0.7546 - val_loss: 0.5203 - val_acc: 0.7514ss: 0.5107 - acc: Epoch 53/100 390870/390870 [==============================] - 71s 183us/step - loss: 0.5107 - acc: 0.7540 - val_loss: 0.5216 - val_acc: 0.7491 Epoch 54/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5097 - acc: 0.7548 - val_loss: 0.5263 - val_acc: 0.7467 Epoch 55/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5091 - acc: 0.7549 - val_loss: 0.5264 - val_acc: 0.7455 Epoch 56/100 390870/390870 [==============================] - 70s 178us/step - loss: 0.5092 - acc: 0.7555 - val_loss: 0.5273 - val_acc: 0.7493 Epoch 57/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5091 - acc: 0.7556 - val_loss: 0.5221 - val_acc: 0.7493 Epoch 58/100 390870/390870 [==============================] - 70s 178us/step - loss: 0.5085 - acc: 0.7552 - val_loss: 0.5192 - val_acc: 0.7517 Epoch 59/100 390870/390870 [==============================] - 70s 178us/step - loss: 0.5089 - acc: 0.7554 - val_loss: 0.5267 - val_acc: 0.7464 Epoch 60/100 390870/390870 [==============================] - 75s 193us/step - loss: 0.5088 - acc: 0.7550 - val_loss: 0.5227 - val_acc: 0.7498 Epoch 61/100 390870/390870 [==============================] - 71s 181us/step - loss: 0.5082 - acc: 0.7555 - val_loss: 0.5191 - val_acc: 0.7517 Epoch 62/100 390870/390870 [==============================] - 67s 173us/step - loss: 0.5086 - acc: 0.7555 - val_loss: 0.5218 - val_acc: 0.7511 Epoch 63/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5088 - acc: 0.7551 - val_loss: 0.5211 - val_acc: 0.7500 Epoch 64/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5085 - acc: 0.7554 - val_loss: 0.5192 - val_acc: 0.7514 Epoch 65/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5078 - acc: 0.7552 - val_loss: 0.5199 - val_acc: 0.7503 Epoch 66/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5080 - acc: 0.7558 - val_loss: 0.5218 - val_acc: 0.7495 Epoch 67/100 390870/390870 [==============================] - 74s 189us/step - loss: 0.5084 - acc: 0.7552 - val_loss: 0.5231 - val_acc: 0.7485 Epoch 68/100 390870/390870 [==============================] - 70s 178us/step - loss: 0.5076 - acc: 0.7558 - val_loss: 0.5306 - val_acc: 0.7455 Epoch 69/100 390870/390870 [==============================] - 73s 187us/step - loss: 0.5073 - acc: 0.7560 - val_loss: 0.5270 - val_acc: 0.7504 Epoch 70/100 390870/390870 [==============================] - 73s 187us/step - loss: 0.5071 - acc: 0.7564 - val_loss: 0.5233 - val_acc: 0.7502 Epoch 71/100 390870/390870 [==============================] - 73s 186us/step - loss: 0.5076 - acc: 0.7558 - val_loss: 0.5326 - val_acc: 0.7471 Epoch 72/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5070 - acc: 0.7566 - val_loss: 0.5188 - val_acc: 0.7518 Epoch 73/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5071 - acc: 0.7562 - val_loss: 0.5230 - val_acc: 0.7499 Epoch 74/100 390870/390870 [==============================] - 70s 178us/step - loss: 0.5064 - acc: 0.7566 - val_loss: 0.5304 - val_acc: 0.7472 Epoch 75/100 390870/390870 [==============================] - 74s 190us/step - loss: 0.5064 - acc: 0.7567 - val_loss: 0.5190 - val_acc: 0.7519 Epoch 76/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5066 - acc: 0.7568 - val_loss: 0.5248 - val_acc: 0.7470 Epoch 77/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5066 - acc: 0.7563 - val_loss: 0.5291 - val_acc: 0.7492 Epoch 78/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5059 - acc: 0.7571 - val_loss: 0.5242 - val_acc: 0.7491 Epoch 79/100 390870/390870 [==============================] - 69s 178us/step - loss: 0.5058 - acc: 0.7569 - val_loss: 0.5234 - val_acc: 0.7493 Epoch 80/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5057 - acc: 0.7571 - val_loss: 0.5217 - val_acc: 0.7515 Epoch 81/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5058 - acc: 0.7571 - val_loss: 0.5193 - val_acc: 0.7515 Epoch 82/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5056 - acc: 0.7568 - val_loss: 0.5207 - val_acc: 0.7497 Epoch 83/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5052 - acc: 0.7577 - val_loss: 0.5324 - val_acc: 0.7428 Epoch 84/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5056 - acc: 0.7568 - val_loss: 0.5264 - val_acc: 0.7496 Epoch 85/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5056 - acc: 0.7573 - val_loss: 0.5225 - val_acc: 0.7511 Epoch 86/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5046 - acc: 0.7576 - val_loss: 0.5194 - val_acc: 0.7522 Epoch 87/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5052 - acc: 0.7569 - val_loss: 0.5229 - val_acc: 0.7509 Epoch 88/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5056 - acc: 0.7569 - val_loss: 0.5284 - val_acc: 0.7495 Epoch 89/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5052 - acc: 0.7573 - val_loss: 0.5270 - val_acc: 0.7508 Epoch 90/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5052 - acc: 0.7574 - val_loss: 0.5216 - val_acc: 0.7515 Epoch 91/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5047 - acc: 0.7582 - val_loss: 0.5327 - val_acc: 0.7487 Epoch 92/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5042 - acc: 0.7577 - val_loss: 0.5241 - val_acc: 0.7491 Epoch 93/100 390870/390870 [==============================] - 98s 251us/step - loss: 0.5041 - acc: 0.7578 - val_loss: 0.5283 - val_acc: 0.7463 Epoch 94/100 390870/390870 [==============================] - 152s 389us/step - loss: 0.5042 - acc: 0.7578 - val_loss: 0.5267 - val_acc: 0.7499 Epoch 95/100 390870/390870 [==============================] - 173s 442us/step - loss: 0.5038 - acc: 0.7579 - val_loss: 0.5319 - val_acc: 0.7487 Epoch 96/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5040 - acc: 0.7580 - val_loss: 0.5238 - val_acc: 0.7511 Epoch 97/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5039 - acc: 0.7580 - val_loss: 0.5258 - val_acc: 0.7498 Epoch 98/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5035 - acc: 0.7583 - val_loss: 0.5252 - val_acc: 0.7506 Epoch 99/100 390870/390870 [==============================] - 73s 188us/step - loss: 0.5043 - acc: 0.7576 - val_loss: 0.5237 - val_acc: 0.7501 Epoch 100/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5038 - acc: 0.7580 - val_loss: 0.5238 - val_acc: 0.7511
<keras.callbacks.History at 0x1e2e3f38630>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all5.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 53s 95us/step [0.5069767637241182, 0.757692707194954]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[0.],
[1.],
[0.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.69 0.43 0.53 50930
1 0.76 0.90 0.83 103063
accuracy 0.75 153993
macro avg 0.72 0.67 0.68 153993
weighted avg 0.74 0.75 0.73 153993
Accuracy for Deep Learning approach: 74.6254699888956
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[21761 29169] [ 9906 93157]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
29
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 500, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 57s 145us/step - loss: 0.6050 - acc: 0.6886 - val_loss: 0.5680 - val_acc: 0.7174 Epoch 2/100 390870/390870 [==============================] - 54s 138us/step - loss: 0.5585 - acc: 0.7214 - val_loss: 0.5550 - val_acc: 0.7256 Epoch 3/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5464 - acc: 0.7309 - val_loss: 0.5400 - val_acc: 0.7387 Epoch 4/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5406 - acc: 0.7345 - val_loss: 0.5605 - val_acc: 0.7164 Epoch 5/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5363 - acc: 0.7370 - val_loss: 0.5421 - val_acc: 0.7324 Epoch 6/100 390870/390870 [==============================] - 54s 137us/step - loss: 0.5341 - acc: 0.7390 - val_loss: 0.5412 - val_acc: 0.7340 Epoch 7/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5315 - acc: 0.7410 - val_loss: 0.5354 - val_acc: 0.7407 Epoch 8/100 390870/390870 [==============================] - 58s 149us/step - loss: 0.5304 - acc: 0.7415 - val_loss: 0.5401 - val_acc: 0.7375 Epoch 9/100 390870/390870 [==============================] - 54s 138us/step - loss: 0.5303 - acc: 0.7414 - val_loss: 0.5321 - val_acc: 0.7445 Epoch 10/100 390870/390870 [==============================] - 53s 134us/step - loss: 0.5277 - acc: 0.7431 - val_loss: 0.5394 - val_acc: 0.7354 Epoch 11/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5261 - acc: 0.7448 - val_loss: 0.5291 - val_acc: 0.7442 Epoch 12/100 390870/390870 [==============================] - 54s 139us/step - loss: 0.5260 - acc: 0.7445 - val_loss: 0.5308 - val_acc: 0.7437 Epoch 13/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5248 - acc: 0.7453 - val_loss: 0.5329 - val_acc: 0.7410 Epoch 14/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5240 - acc: 0.7463 - val_loss: 0.5294 - val_acc: 0.7449 Epoch 15/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5226 - acc: 0.7468 - val_loss: 0.5345 - val_acc: 0.7397 Epoch 16/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5220 - acc: 0.7478 - val_loss: 0.5270 - val_acc: 0.7451 Epoch 17/100 390870/390870 [==============================] - 54s 139us/step - loss: 0.5216 - acc: 0.7480 - val_loss: 0.5347 - val_acc: 0.7375 Epoch 18/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5216 - acc: 0.7479 - val_loss: 0.5338 - val_acc: 0.7427 Epoch 19/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5198 - acc: 0.7488 - val_loss: 0.5268 - val_acc: 0.7446 Epoch 20/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5201 - acc: 0.7490 - val_loss: 0.5260 - val_acc: 0.7464 Epoch 21/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5199 - acc: 0.7491 - val_loss: 0.5272 - val_acc: 0.7472 Epoch 22/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5187 - acc: 0.7503 - val_loss: 0.5312 - val_acc: 0.7460 Epoch 23/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5182 - acc: 0.7498 - val_loss: 0.5255 - val_acc: 0.7470 Epoch 24/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5185 - acc: 0.7500 - val_loss: 0.5231 - val_acc: 0.7478 Epoch 25/100 390870/390870 [==============================] - 55s 142us/step - loss: 0.5164 - acc: 0.7515 - val_loss: 0.5231 - val_acc: 0.7482 Epoch 26/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5180 - acc: 0.7499 - val_loss: 0.5375 - val_acc: 0.7416 Epoch 27/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5158 - acc: 0.7514 - val_loss: 0.5224 - val_acc: 0.7485 Epoch 28/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5167 - acc: 0.7513 - val_loss: 0.5280 - val_acc: 0.7449 Epoch 29/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5155 - acc: 0.7518 - val_loss: 0.5221 - val_acc: 0.7489 Epoch 30/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5144 - acc: 0.7524 - val_loss: 0.5238 - val_acc: 0.7487 Epoch 31/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5143 - acc: 0.7524 - val_loss: 0.5236 - val_acc: 0.7475 Epoch 32/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5152 - acc: 0.7516 - val_loss: 0.5229 - val_acc: 0.7478 Epoch 33/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5146 - acc: 0.7525 - val_loss: 0.5210 - val_acc: 0.7502 Epoch 34/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5137 - acc: 0.7525 - val_loss: 0.5263 - val_acc: 0.7480 Epoch 35/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5141 - acc: 0.7520 - val_loss: 0.5218 - val_acc: 0.7504 Epoch 36/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5126 - acc: 0.7537 - val_loss: 0.5221 - val_acc: 0.7487 Epoch 37/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5128 - acc: 0.7533 - val_loss: 0.5234 - val_acc: 0.7476 Epoch 38/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5128 - acc: 0.7529 - val_loss: 0.5282 - val_acc: 0.7457 Epoch 39/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5127 - acc: 0.7532 - val_loss: 0.5275 - val_acc: 0.7450 Epoch 40/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.5123 - acc: 0.7532 - val_loss: 0.5284 - val_acc: 0.7454 Epoch 41/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5106 - acc: 0.7540 - val_loss: 0.5223 - val_acc: 0.7506 Epoch 42/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5110 - acc: 0.7539 - val_loss: 0.5356 - val_acc: 0.7429 Epoch 43/100 390870/390870 [==============================] - 53s 135us/step - loss: 0.5106 - acc: 0.7546 - val_loss: 0.5221 - val_acc: 0.7499 Epoch 44/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5114 - acc: 0.7540 - val_loss: 0.5223 - val_acc: 0.7497 Epoch 45/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5099 - acc: 0.7547 - val_loss: 0.5279 - val_acc: 0.7487 Epoch 46/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5097 - acc: 0.7551 - val_loss: 0.5227 - val_acc: 0.7501 Epoch 47/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5096 - acc: 0.7554 - val_loss: 0.5270 - val_acc: 0.7467 Epoch 48/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5095 - acc: 0.7552 - val_loss: 0.5200 - val_acc: 0.7490 Epoch 49/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5096 - acc: 0.7549 - val_loss: 0.5226 - val_acc: 0.7484 Epoch 50/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5095 - acc: 0.7551 - val_loss: 0.5250 - val_acc: 0.7493 Epoch 51/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5080 - acc: 0.7559 - val_loss: 0.5202 - val_acc: 0.7504 Epoch 52/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5088 - acc: 0.7552 - val_loss: 0.5212 - val_acc: 0.7499 Epoch 53/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5083 - acc: 0.7554 - val_loss: 0.5253 - val_acc: 0.7473 Epoch 54/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5082 - acc: 0.7559 - val_loss: 0.5205 - val_acc: 0.7509 Epoch 55/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.5081 - acc: 0.7554 - val_loss: 0.5253 - val_acc: 0.7487 Epoch 56/100 390870/390870 [==============================] - 51s 129us/step - loss: 0.5080 - acc: 0.7555 - val_loss: 0.5207 - val_acc: 0.7509 Epoch 57/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5069 - acc: 0.7568 - val_loss: 0.5214 - val_acc: 0.7497 Epoch 58/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.5073 - acc: 0.7564 - val_loss: 0.5199 - val_acc: 0.7509 Epoch 59/100 390870/390870 [==============================] - 77s 198us/step - loss: 0.5079 - acc: 0.7558 - val_loss: 0.5295 - val_acc: 0.7464 Epoch 60/100 390870/390870 [==============================] - 54s 138us/step - loss: 0.5064 - acc: 0.7564 - val_loss: 0.5291 - val_acc: 0.7469 Epoch 61/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5065 - acc: 0.7568 - val_loss: 0.5185 - val_acc: 0.7520 Epoch 62/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5065 - acc: 0.7568 - val_loss: 0.5221 - val_acc: 0.7496 Epoch 63/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5059 - acc: 0.7571 - val_loss: 0.5276 - val_acc: 0.7488 Epoch 64/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5057 - acc: 0.7574 - val_loss: 0.5204 - val_acc: 0.7505 Epoch 65/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5055 - acc: 0.7577 - val_loss: 0.5293 - val_acc: 0.7427 Epoch 66/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5046 - acc: 0.7582 - val_loss: 0.5190 - val_acc: 0.7508 Epoch 67/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5052 - acc: 0.7575 - val_loss: 0.5196 - val_acc: 0.7517 Epoch 68/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5049 - acc: 0.7580 - val_loss: 0.5246 - val_acc: 0.7480 Epoch 69/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5054 - acc: 0.7577 - val_loss: 0.5297 - val_acc: 0.7441 Epoch 70/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5044 - acc: 0.7576 - val_loss: 0.5272 - val_acc: 0.7490 Epoch 71/100 390870/390870 [==============================] - 52s 132us/step - loss: 0.5058 - acc: 0.7574 - val_loss: 0.5225 - val_acc: 0.7481 Epoch 72/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5039 - acc: 0.7583 - val_loss: 0.5221 - val_acc: 0.7486 Epoch 73/100 390870/390870 [==============================] - 60s 153us/step - loss: 0.5041 - acc: 0.7584 - val_loss: 0.5193 - val_acc: 0.7506 Epoch 74/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5035 - acc: 0.7582 - val_loss: 0.5223 - val_acc: 0.7491 Epoch 75/100 390870/390870 [==============================] - 51s 129us/step - loss: 0.5033 - acc: 0.7588 - val_loss: 0.5227 - val_acc: 0.7506 Epoch 76/100 390870/390870 [==============================] - 51s 129us/step - loss: 0.5033 - acc: 0.7582 - val_loss: 0.5282 - val_acc: 0.7470 Epoch 77/100 390870/390870 [==============================] - 55s 141us/step - loss: 0.5042 - acc: 0.7580 - val_loss: 0.5244 - val_acc: 0.7499 Epoch 78/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5034 - acc: 0.7586 - val_loss: 0.5261 - val_acc: 0.7466 Epoch 79/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5027 - acc: 0.7595 - val_loss: 0.5263 - val_acc: 0.7494 Epoch 80/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5028 - acc: 0.7592 - val_loss: 0.5273 - val_acc: 0.7460 Epoch 81/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5022 - acc: 0.7593 - val_loss: 0.5257 - val_acc: 0.7488 Epoch 82/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5025 - acc: 0.7589 - val_loss: 0.5220 - val_acc: 0.7502 Epoch 83/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5019 - acc: 0.7593 - val_loss: 0.5270 - val_acc: 0.7478 Epoch 84/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5023 - acc: 0.7594 - val_loss: 0.5259 - val_acc: 0.7492 Epoch 85/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5018 - acc: 0.7592 - val_loss: 0.5267 - val_acc: 0.7498 Epoch 86/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5021 - acc: 0.7591 - val_loss: 0.5302 - val_acc: 0.7445 Epoch 87/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5013 - acc: 0.7597 - val_loss: 0.5209 - val_acc: 0.7512 Epoch 88/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5010 - acc: 0.7596 - val_loss: 0.5268 - val_acc: 0.7466 Epoch 89/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.5019 - acc: 0.7594 - val_loss: 0.5236 - val_acc: 0.7510 Epoch 90/100 390870/390870 [==============================] - 51s 129us/step - loss: 0.5012 - acc: 0.7598 - val_loss: 0.5240 - val_acc: 0.7489 Epoch 91/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5016 - acc: 0.7598 - val_loss: 0.5236 - val_acc: 0.7516 Epoch 92/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5013 - acc: 0.7596 - val_loss: 0.5237 - val_acc: 0.7501 Epoch 93/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5009 - acc: 0.7597 - val_loss: 0.5250 - val_acc: 0.7505 Epoch 94/100 390870/390870 [==============================] - 55s 142us/step - loss: 0.5003 - acc: 0.7599 - val_loss: 0.5223 - val_acc: 0.7501 Epoch 95/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5002 - acc: 0.7598 - val_loss: 0.5274 - val_acc: 0.7493 Epoch 96/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5002 - acc: 0.7599 - val_loss: 0.5247 - val_acc: 0.7497 Epoch 97/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5000 - acc: 0.7601 - val_loss: 0.5289 - val_acc: 0.7498 Epoch 98/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.4997 - acc: 0.7612 - val_loss: 0.5273 - val_acc: 0.7504 Epoch 99/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.4997 - acc: 0.7605 - val_loss: 0.5269 - val_acc: 0.7495 Epoch 100/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.4994 - acc: 0.7607 - val_loss: 0.5307 - val_acc: 0.7475
<keras.callbacks.History at 0x28588237f28>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all6.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 46s 83us/step [0.5109344036041277, 0.7546177733688839]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[1.],
[1.],
[1.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.73 0.34 0.46 50930
1 0.74 0.94 0.83 103063
accuracy 0.74 153993
macro avg 0.74 0.64 0.65 153993
weighted avg 0.74 0.74 0.71 153993
Accuracy for Deep Learning approach: 73.97803796276455
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[17292 33638] [ 6434 96629]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 6
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
34
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 71s 183us/step - loss: 0.5860 - acc: 0.7012 - val_loss: 0.5567 - val_acc: 0.7211 Epoch 2/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5531 - acc: 0.7259 - val_loss: 0.5461 - val_acc: 0.7325 Epoch 3/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5453 - acc: 0.7313 - val_loss: 0.5470 - val_acc: 0.7292 Epoch 4/100 390870/390870 [==============================] - 65s 167us/step - loss: 0.5406 - acc: 0.7342 - val_loss: 0.5556 - val_acc: 0.7176 Epoch 5/100 390870/390870 [==============================] - 65s 166us/step - loss: 0.5377 - acc: 0.7368 - val_loss: 0.5365 - val_acc: 0.7417 Epoch 6/100 390870/390870 [==============================] - 70s 179us/step - loss: 0.5350 - acc: 0.7385 - val_loss: 0.5380 - val_acc: 0.7371 Epoch 7/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5324 - acc: 0.7404 - val_loss: 0.5296 - val_acc: 0.7453 Epoch 8/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5307 - acc: 0.7412 - val_loss: 0.5569 - val_acc: 0.7224 Epoch 9/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5291 - acc: 0.7430 - val_loss: 0.5283 - val_acc: 0.7449 Epoch 10/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5279 - acc: 0.7433 - val_loss: 0.5364 - val_acc: 0.7405 Epoch 11/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5261 - acc: 0.7454 - val_loss: 0.5308 - val_acc: 0.7451 Epoch 12/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5257 - acc: 0.7454 - val_loss: 0.5291 - val_acc: 0.7446 Epoch 13/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5246 - acc: 0.7458 - val_loss: 0.5282 - val_acc: 0.7453 Epoch 14/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5236 - acc: 0.7467 - val_loss: 0.5402 - val_acc: 0.7408 Epoch 15/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5226 - acc: 0.7469 - val_loss: 0.5656 - val_acc: 0.7069 Epoch 16/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5218 - acc: 0.7479 - val_loss: 0.5318 - val_acc: 0.7428 Epoch 17/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5214 - acc: 0.7483 - val_loss: 0.5306 - val_acc: 0.7426 Epoch 18/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5205 - acc: 0.7484 - val_loss: 0.5240 - val_acc: 0.7477 Epoch 19/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5203 - acc: 0.7486 - val_loss: 0.5261 - val_acc: 0.7462 Epoch 20/100 390870/390870 [==============================] - 72s 183us/step - loss: 0.5192 - acc: 0.7494 - val_loss: 0.5250 - val_acc: 0.7462 Epoch 21/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5190 - acc: 0.7495 - val_loss: 0.5293 - val_acc: 0.7428 Epoch 22/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5178 - acc: 0.7504 - val_loss: 0.5246 - val_acc: 0.7484 Epoch 23/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5172 - acc: 0.7507 - val_loss: 0.5313 - val_acc: 0.7447 Epoch 24/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5168 - acc: 0.7506 - val_loss: 0.5272 - val_acc: 0.7495 Epoch 25/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5168 - acc: 0.7504 - val_loss: 0.5352 - val_acc: 0.7368 Epoch 26/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5159 - acc: 0.7516 - val_loss: 0.5378 - val_acc: 0.7412 Epoch 27/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5163 - acc: 0.7510 - val_loss: 0.5219 - val_acc: 0.7488 Epoch 28/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5150 - acc: 0.7519 - val_loss: 0.5265 - val_acc: 0.7484 Epoch 29/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5142 - acc: 0.7520 - val_loss: 0.5203 - val_acc: 0.7489 Epoch 30/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5146 - acc: 0.7520 - val_loss: 0.5282 - val_acc: 0.7444 Epoch 31/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5138 - acc: 0.7522 - val_loss: 0.5228 - val_acc: 0.7481 Epoch 32/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5147 - acc: 0.7522 - val_loss: 0.5244 - val_acc: 0.7488 Epoch 33/100 390870/390870 [==============================] - 71s 181us/step - loss: 0.5133 - acc: 0.7528 - val_loss: 0.5283 - val_acc: 0.7472 Epoch 34/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5128 - acc: 0.7530 - val_loss: 0.5404 - val_acc: 0.7392 Epoch 35/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5131 - acc: 0.7530 - val_loss: 0.5231 - val_acc: 0.7475 Epoch 36/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5124 - acc: 0.7530 - val_loss: 0.5216 - val_acc: 0.7472 Epoch 37/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5121 - acc: 0.7531 - val_loss: 0.5203 - val_acc: 0.7502 Epoch 38/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5122 - acc: 0.7538 - val_loss: 0.5307 - val_acc: 0.7488 Epoch 39/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5108 - acc: 0.7542 - val_loss: 0.5241 - val_acc: 0.7482 Epoch 40/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5113 - acc: 0.7533 - val_loss: 0.5227 - val_acc: 0.7492 Epoch 41/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5105 - acc: 0.7546 - val_loss: 0.5185 - val_acc: 0.7502 Epoch 42/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5106 - acc: 0.7542 - val_loss: 0.5242 - val_acc: 0.7478 Epoch 43/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5099 - acc: 0.7543 - val_loss: 0.5295 - val_acc: 0.7486 Epoch 44/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5097 - acc: 0.7545 - val_loss: 0.5188 - val_acc: 0.7514 Epoch 45/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5098 - acc: 0.7547 - val_loss: 0.5262 - val_acc: 0.7463 Epoch 46/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5092 - acc: 0.7556 - val_loss: 0.5207 - val_acc: 0.7503 Epoch 47/100 390870/390870 [==============================] - 72s 183us/step - loss: 0.5089 - acc: 0.7551 - val_loss: 0.5212 - val_acc: 0.7496 Epoch 48/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5089 - acc: 0.7551 - val_loss: 0.5202 - val_acc: 0.7500 Epoch 49/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5085 - acc: 0.7552 - val_loss: 0.5279 - val_acc: 0.7454 Epoch 50/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5083 - acc: 0.7557 - val_loss: 0.5201 - val_acc: 0.7491 Epoch 51/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5079 - acc: 0.7560 - val_loss: 0.5370 - val_acc: 0.7446 Epoch 52/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5075 - acc: 0.7559 - val_loss: 0.5251 - val_acc: 0.7470 Epoch 53/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5079 - acc: 0.7562 - val_loss: 0.5235 - val_acc: 0.7495 Epoch 54/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5073 - acc: 0.7567 - val_loss: 0.5264 - val_acc: 0.7460 Epoch 55/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5070 - acc: 0.7565 - val_loss: 0.5272 - val_acc: 0.7467 Epoch 56/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5071 - acc: 0.7565 - val_loss: 0.5214 - val_acc: 0.7506 Epoch 57/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5068 - acc: 0.7563 - val_loss: 0.5289 - val_acc: 0.7469 Epoch 58/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5070 - acc: 0.7561 - val_loss: 0.5300 - val_acc: 0.7478 Epoch 59/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5062 - acc: 0.7567 - val_loss: 0.5801 - val_acc: 0.7259 Epoch 60/100 390870/390870 [==============================] - 70s 180us/step - loss: 0.5056 - acc: 0.7570 - val_loss: 0.5222 - val_acc: 0.7502 Epoch 61/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5059 - acc: 0.7568 - val_loss: 0.5229 - val_acc: 0.7499 Epoch 62/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5056 - acc: 0.7569 - val_loss: 0.5210 - val_acc: 0.7501 Epoch 63/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5055 - acc: 0.7574 - val_loss: 0.5225 - val_acc: 0.7503 Epoch 64/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5059 - acc: 0.7569 - val_loss: 0.5217 - val_acc: 0.7521 Epoch 65/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5050 - acc: 0.7572 - val_loss: 0.5291 - val_acc: 0.7444 Epoch 66/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5047 - acc: 0.7576 - val_loss: 0.5266 - val_acc: 0.7461 Epoch 67/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5047 - acc: 0.7576 - val_loss: 0.5246 - val_acc: 0.7491 Epoch 68/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5047 - acc: 0.7573 - val_loss: 0.5227 - val_acc: 0.7500 Epoch 69/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5041 - acc: 0.7581 - val_loss: 0.5244 - val_acc: 0.7497 Epoch 70/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5041 - acc: 0.7578 - val_loss: 0.5185 - val_acc: 0.7517 Epoch 71/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5045 - acc: 0.7575 - val_loss: 0.5247 - val_acc: 0.7488 Epoch 72/100 390870/390870 [==============================] - 66s 168us/step - loss: 0.5039 - acc: 0.7584 - val_loss: 0.5315 - val_acc: 0.7481 Epoch 73/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5038 - acc: 0.7583 - val_loss: 0.5238 - val_acc: 0.7498 Epoch 74/100 390870/390870 [==============================] - 72s 185us/step - loss: 0.5034 - acc: 0.7580 - val_loss: 0.5238 - val_acc: 0.7497 Epoch 75/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5033 - acc: 0.7587 - val_loss: 0.5217 - val_acc: 0.7495 Epoch 76/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5028 - acc: 0.7582 - val_loss: 0.5268 - val_acc: 0.7469 Epoch 77/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5033 - acc: 0.7584 - val_loss: 0.5227 - val_acc: 0.7494 Epoch 78/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5028 - acc: 0.7586 - val_loss: 0.5263 - val_acc: 0.7477 Epoch 79/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5022 - acc: 0.7589 - val_loss: 0.5261 - val_acc: 0.7477 Epoch 80/100 390870/390870 [==============================] - 67s 170us/step - loss: 0.5023 - acc: 0.7590 - val_loss: 0.5231 - val_acc: 0.7491 Epoch 81/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5022 - acc: 0.7592 - val_loss: 0.5223 - val_acc: 0.7512 Epoch 82/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.5018 - acc: 0.7593 - val_loss: 0.5234 - val_acc: 0.7496 Epoch 83/100 390870/390870 [==============================] - 173s 443us/step - loss: 0.5016 - acc: 0.7592 - val_loss: 0.5249 - val_acc: 0.7528 Epoch 84/100 390870/390870 [==============================] - 69s 176us/step - loss: 0.5019 - acc: 0.7590 - val_loss: 0.5272 - val_acc: 0.7499 Epoch 85/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5018 - acc: 0.7591 - val_loss: 0.5320 - val_acc: 0.7462 Epoch 86/100 390870/390870 [==============================] - 75s 193us/step - loss: 0.5014 - acc: 0.7593 - val_loss: 0.5244 - val_acc: 0.7509 Epoch 87/100 390870/390870 [==============================] - 71s 181us/step - loss: 0.5011 - acc: 0.7597 - val_loss: 0.5241 - val_acc: 0.7508 Epoch 88/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5008 - acc: 0.7597 - val_loss: 0.5280 - val_acc: 0.7500 Epoch 89/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5007 - acc: 0.7595 - val_loss: 0.5217 - val_acc: 0.7519 Epoch 90/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5014 - acc: 0.7594 - val_loss: 0.5226 - val_acc: 0.7497 Epoch 91/100 390870/390870 [==============================] - 68s 174us/step - loss: 0.5009 - acc: 0.7596 - val_loss: 0.5245 - val_acc: 0.7496 Epoch 92/100 390870/390870 [==============================] - 67s 173us/step - loss: 0.5010 - acc: 0.7590 - val_loss: 0.5284 - val_acc: 0.7489 Epoch 93/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.5005 - acc: 0.7598 - val_loss: 0.5343 - val_acc: 0.7456 Epoch 94/100 390870/390870 [==============================] - 67s 171us/step - loss: 0.5003 - acc: 0.7597 - val_loss: 0.5248 - val_acc: 0.7487 Epoch 95/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5003 - acc: 0.7601 - val_loss: 0.5264 - val_acc: 0.7486 Epoch 96/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.4998 - acc: 0.7605 - val_loss: 0.5259 - val_acc: 0.7515 Epoch 97/100 390870/390870 [==============================] - 66s 169us/step - loss: 0.5000 - acc: 0.7607 - val_loss: 0.5246 - val_acc: 0.7491 Epoch 98/100 390870/390870 [==============================] - 66s 170us/step - loss: 0.4996 - acc: 0.7610 - val_loss: 0.5377 - val_acc: 0.7410 Epoch 99/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5007 - acc: 0.7601 - val_loss: 0.5272 - val_acc: 0.7484 Epoch 100/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.4996 - acc: 0.7608 - val_loss: 0.5210 - val_acc: 0.7509
<keras.callbacks.History at 0x28588c76ac8>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all7.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 48s 87us/step [0.5033024442936678, 0.7597557961701017]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[1.],
[1.],
[0.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.69 0.42 0.52 50930
1 0.76 0.91 0.83 103063
accuracy 0.74 153993
macro avg 0.72 0.66 0.67 153993
weighted avg 0.74 0.74 0.72 153993
Accuracy for Deep Learning approach: 74.48585325306995
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[21192 29738] [ 9552 93511]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 6
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
34
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 500, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 60s 153us/step - loss: 0.6000 - acc: 0.6893 - val_loss: 0.5623 - val_acc: 0.7238 Epoch 2/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5547 - acc: 0.7240 - val_loss: 0.5505 - val_acc: 0.7272 Epoch 3/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.5446 - acc: 0.7316 - val_loss: 0.5395 - val_acc: 0.7382 Epoch 4/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5374 - acc: 0.7368 - val_loss: 0.5323 - val_acc: 0.7432 Epoch 5/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5366 - acc: 0.7371 - val_loss: 0.5371 - val_acc: 0.7398 Epoch 6/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5339 - acc: 0.7391 - val_loss: 0.5352 - val_acc: 0.7395 Epoch 7/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5312 - acc: 0.7411 - val_loss: 0.5340 - val_acc: 0.7425 Epoch 8/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5290 - acc: 0.7423 - val_loss: 0.5493 - val_acc: 0.7272 Epoch 9/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5284 - acc: 0.7431 - val_loss: 0.5344 - val_acc: 0.7406 Epoch 10/100 390870/390870 [==============================] - 57s 145us/step - loss: 0.5262 - acc: 0.7445 - val_loss: 0.5320 - val_acc: 0.7417 Epoch 11/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5248 - acc: 0.7456 - val_loss: 0.5352 - val_acc: 0.7399 Epoch 12/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5244 - acc: 0.7457 - val_loss: 0.5249 - val_acc: 0.7468 Epoch 13/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5227 - acc: 0.7468 - val_loss: 0.5284 - val_acc: 0.7451 Epoch 14/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5223 - acc: 0.7481 - val_loss: 0.5247 - val_acc: 0.7475 Epoch 15/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5225 - acc: 0.7475 - val_loss: 0.5307 - val_acc: 0.7437 Epoch 16/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5212 - acc: 0.7476 - val_loss: 0.5321 - val_acc: 0.7429 Epoch 17/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5208 - acc: 0.7485 - val_loss: 0.5258 - val_acc: 0.7450 Epoch 18/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5198 - acc: 0.7484 - val_loss: 0.5278 - val_acc: 0.7457 Epoch 19/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5184 - acc: 0.7496 - val_loss: 0.5233 - val_acc: 0.7486 Epoch 20/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5182 - acc: 0.7496 - val_loss: 0.5240 - val_acc: 0.7476 Epoch 21/100 390870/390870 [==============================] - 50s 127us/step - loss: 0.5179 - acc: 0.7498 - val_loss: 0.5236 - val_acc: 0.7484 Epoch 22/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5163 - acc: 0.7512 - val_loss: 0.5221 - val_acc: 0.7483 Epoch 23/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5160 - acc: 0.7514 - val_loss: 0.5288 - val_acc: 0.7450 Epoch 24/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5153 - acc: 0.7514 - val_loss: 0.5319 - val_acc: 0.7449 Epoch 25/100 390870/390870 [==============================] - 56s 144us/step - loss: 0.5151 - acc: 0.7520 - val_loss: 0.5248 - val_acc: 0.7468 Epoch 26/100 390870/390870 [==============================] - 122s 311us/step - loss: 0.5141 - acc: 0.7520 - val_loss: 0.5198 - val_acc: 0.7498 Epoch 27/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5146 - acc: 0.7514 - val_loss: 0.5223 - val_acc: 0.7492 Epoch 28/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5138 - acc: 0.7523 - val_loss: 0.5232 - val_acc: 0.7480 Epoch 29/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5123 - acc: 0.7533 - val_loss: 0.5196 - val_acc: 0.7512 Epoch 30/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5130 - acc: 0.7525 - val_loss: 0.5197 - val_acc: 0.7503 Epoch 31/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5119 - acc: 0.7541 - val_loss: 0.5242 - val_acc: 0.7493 Epoch 32/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5123 - acc: 0.7531 - val_loss: 0.5248 - val_acc: 0.7480 Epoch 33/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5113 - acc: 0.7539 - val_loss: 0.5227 - val_acc: 0.7486 Epoch 34/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5113 - acc: 0.7541 - val_loss: 0.5208 - val_acc: 0.7507 Epoch 35/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5114 - acc: 0.7531 - val_loss: 0.5202 - val_acc: 0.7488 Epoch 36/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5106 - acc: 0.7544 - val_loss: 0.5245 - val_acc: 0.7461 Epoch 37/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5109 - acc: 0.7537 - val_loss: 0.5253 - val_acc: 0.7459 Epoch 38/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5099 - acc: 0.7546 - val_loss: 0.5179 - val_acc: 0.7513 Epoch 39/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5088 - acc: 0.7551 - val_loss: 0.5195 - val_acc: 0.7495 Epoch 40/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5093 - acc: 0.7549 - val_loss: 0.5187 - val_acc: 0.7514 Epoch 41/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5084 - acc: 0.7555 - val_loss: 0.5247 - val_acc: 0.7485 Epoch 42/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.5084 - acc: 0.7553 - val_loss: 0.5258 - val_acc: 0.7479 Epoch 43/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5077 - acc: 0.7554 - val_loss: 0.5195 - val_acc: 0.7511 Epoch 44/100 390870/390870 [==============================] - 56s 144us/step - loss: 0.5073 - acc: 0.7555 - val_loss: 0.5191 - val_acc: 0.7501 Epoch 45/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5077 - acc: 0.7554 - val_loss: 0.5202 - val_acc: 0.7508 Epoch 46/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5069 - acc: 0.7567 - val_loss: 0.5241 - val_acc: 0.7480 Epoch 47/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5073 - acc: 0.7561 - val_loss: 0.5229 - val_acc: 0.7477 Epoch 48/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5076 - acc: 0.7555 - val_loss: 0.5235 - val_acc: 0.7488 Epoch 49/100 390870/390870 [==============================] - 53s 134us/step - loss: 0.5053 - acc: 0.7571 - val_loss: 0.5196 - val_acc: 0.7500 Epoch 50/100 390870/390870 [==============================] - 202s 517us/step - loss: 0.5063 - acc: 0.7562 - val_loss: 0.5226 - val_acc: 0.7473 Epoch 51/100 390870/390870 [==============================] - 51s 129us/step - loss: 0.5055 - acc: 0.7571 - val_loss: 0.5298 - val_acc: 0.7420 Epoch 52/100 390870/390870 [==============================] - 51s 130us/step - loss: 0.5054 - acc: 0.7571 - val_loss: 0.5174 - val_acc: 0.7514 Epoch 53/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5054 - acc: 0.7568 - val_loss: 0.5236 - val_acc: 0.7496 Epoch 54/100 390870/390870 [==============================] - 50s 127us/step - loss: 0.5045 - acc: 0.7576 - val_loss: 0.5235 - val_acc: 0.7474 Epoch 55/100 390870/390870 [==============================] - 59s 150us/step - loss: 0.5046 - acc: 0.7576 - val_loss: 0.5263 - val_acc: 0.7475 Epoch 56/100 390870/390870 [==============================] - 61s 156us/step - loss: 0.5040 - acc: 0.7581 - val_loss: 0.5194 - val_acc: 0.7503 Epoch 57/100 390870/390870 [==============================] - 55s 142us/step - loss: 0.5051 - acc: 0.7571 - val_loss: 0.5175 - val_acc: 0.7527 Epoch 58/100 390870/390870 [==============================] - 58s 147us/step - loss: 0.5044 - acc: 0.7573 - val_loss: 0.5236 - val_acc: 0.7504 Epoch 59/100 390870/390870 [==============================] - 53s 136us/step - loss: 0.5040 - acc: 0.7578 - val_loss: 0.5192 - val_acc: 0.7520 Epoch 60/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5034 - acc: 0.7588 - val_loss: 0.5231 - val_acc: 0.7494 Epoch 61/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5031 - acc: 0.7582 - val_loss: 0.5236 - val_acc: 0.7493 Epoch 62/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5036 - acc: 0.7582 - val_loss: 0.5184 - val_acc: 0.7514 Epoch 63/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5038 - acc: 0.7582 - val_loss: 0.5226 - val_acc: 0.7502 Epoch 64/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5035 - acc: 0.7580 - val_loss: 0.5202 - val_acc: 0.7512 Epoch 65/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5029 - acc: 0.7586 - val_loss: 0.5237 - val_acc: 0.7473 Epoch 66/100 390870/390870 [==============================] - 52s 134us/step - loss: 0.5023 - acc: 0.7586 - val_loss: 0.5242 - val_acc: 0.7488 Epoch 67/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5031 - acc: 0.7582 - val_loss: 0.5237 - val_acc: 0.7483 Epoch 68/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5023 - acc: 0.7591 - val_loss: 0.5277 - val_acc: 0.7460 Epoch 69/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5024 - acc: 0.7587 - val_loss: 0.5266 - val_acc: 0.7505 Epoch 70/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5028 - acc: 0.7586 - val_loss: 0.5228 - val_acc: 0.7484 Epoch 71/100 390870/390870 [==============================] - 50s 127us/step - loss: 0.5015 - acc: 0.7593 - val_loss: 0.5199 - val_acc: 0.7515 Epoch 72/100 390870/390870 [==============================] - 50s 127us/step - loss: 0.5018 - acc: 0.7589 - val_loss: 0.5213 - val_acc: 0.7502 Epoch 73/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.5016 - acc: 0.7593 - val_loss: 0.5244 - val_acc: 0.7480 Epoch 74/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5013 - acc: 0.7595 - val_loss: 0.5251 - val_acc: 0.7483 Epoch 75/100 390870/390870 [==============================] - 54s 138us/step - loss: 0.5010 - acc: 0.7599 - val_loss: 0.5296 - val_acc: 0.7451 Epoch 76/100 390870/390870 [==============================] - 55s 140us/step - loss: 0.5010 - acc: 0.7598 - val_loss: 0.5251 - val_acc: 0.7493 Epoch 77/100 390870/390870 [==============================] - 64s 163us/step - loss: 0.5003 - acc: 0.7599 - val_loss: 0.5200 - val_acc: 0.7535 Epoch 78/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.5009 - acc: 0.7596 - val_loss: 0.5217 - val_acc: 0.7486 Epoch 79/100 390870/390870 [==============================] - 68s 173us/step - loss: 0.5000 - acc: 0.7596 - val_loss: 0.5240 - val_acc: 0.7490 Epoch 80/100 390870/390870 [==============================] - 76s 196us/step - loss: 0.5000 - acc: 0.7600 - val_loss: 0.5238 - val_acc: 0.7480 Epoch 81/100 390870/390870 [==============================] - 169s 433us/step - loss: 0.4997 - acc: 0.7599 - val_loss: 0.5271 - val_acc: 0.7491 Epoch 82/100 390870/390870 [==============================] - 59s 150us/step - loss: 0.4992 - acc: 0.7605 - val_loss: 0.5217 - val_acc: 0.7517 Epoch 83/100 390870/390870 [==============================] - 91s 234us/step - loss: 0.4992 - acc: 0.7603 - val_loss: 0.5226 - val_acc: 0.7511 Epoch 84/100 390870/390870 [==============================] - 69s 177us/step - loss: 0.5003 - acc: 0.7594 - val_loss: 0.5291 - val_acc: 0.7458 Epoch 85/100 390870/390870 [==============================] - 50s 128us/step - loss: 0.4991 - acc: 0.7601 - val_loss: 0.5210 - val_acc: 0.7500 Epoch 86/100 390870/390870 [==============================] - 51s 131us/step - loss: 0.4992 - acc: 0.7599 - val_loss: 0.5225 - val_acc: 0.7509 Epoch 87/100 390870/390870 [==============================] - 52s 133us/step - loss: 0.4989 - acc: 0.7604 - val_loss: 0.5234 - val_acc: 0.7493 Epoch 88/100 390870/390870 [==============================] - 53s 137us/step - loss: 0.4979 - acc: 0.7608 - val_loss: 0.5315 - val_acc: 0.7489 Epoch 89/100 390870/390870 [==============================] - 61s 156us/step - loss: 0.4985 - acc: 0.7607 - val_loss: 0.5237 - val_acc: 0.7487 Epoch 90/100 390870/390870 [==============================] - 74s 188us/step - loss: 0.4992 - acc: 0.7602 - val_loss: 0.5223 - val_acc: 0.7511 Epoch 91/100 390870/390870 [==============================] - 85s 216us/step - loss: 0.4984 - acc: 0.7611 - val_loss: 0.5245 - val_acc: 0.7499 Epoch 92/100 390870/390870 [==============================] - 67s 172us/step - loss: 0.4972 - acc: 0.7615 - val_loss: 0.5271 - val_acc: 0.7492 Epoch 93/100 390870/390870 [==============================] - 61s 155us/step - loss: 0.4976 - acc: 0.7611 - val_loss: 0.5268 - val_acc: 0.7457 Epoch 94/100 390870/390870 [==============================] - 60s 152us/step - loss: 0.4977 - acc: 0.7612 - val_loss: 0.5360 - val_acc: 0.7415 Epoch 95/100 390870/390870 [==============================] - 60s 153us/step - loss: 0.4979 - acc: 0.7608 - val_loss: 0.5229 - val_acc: 0.7495 Epoch 96/100 390870/390870 [==============================] - 54s 138us/step - loss: 0.4976 - acc: 0.7613 - val_loss: 0.5265 - val_acc: 0.7504 Epoch 97/100 390870/390870 [==============================] - 56s 142us/step - loss: 0.4971 - acc: 0.7614 - val_loss: 0.5264 - val_acc: 0.7495 Epoch 98/100 390870/390870 [==============================] - 58s 147us/step - loss: 0.4969 - acc: 0.7613 - val_loss: 0.5283 - val_acc: 0.7470 Epoch 99/100 390870/390870 [==============================] - 50s 127us/step - loss: 0.4975 - acc: 0.7614 - val_loss: 0.5328 - val_acc: 0.7460 Epoch 100/100 390870/390870 [==============================] - 50s 127us/step - loss: 0.4967 - acc: 0.7620 - val_loss: 0.5406 - val_acc: 0.7454
<keras.callbacks.History at 0x2859abf6860>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all8.h5")
# evaluation of the model training
classifier = _load_model("../models/trained_deep_neural_network_all8.h5") # DEBUG!!!
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 54s 97us/step [0.5078941215270898, 0.7580132739713417]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[0.],
[0.],
[1.],
...,
[1.],
[1.],
[1.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.67 0.44 0.53 50930
1 0.76 0.89 0.82 103063
accuracy 0.74 153993
macro avg 0.72 0.67 0.68 153993
weighted avg 0.73 0.74 0.73 153993
Accuracy for Deep Learning approach: 74.25792081458248
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[22257 28673] [10968 92095]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_2.pickle')
train_set.shape
(558386, 2774)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 8
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
25
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 97s 249us/step - loss: 0.5884 - acc: 0.6991 - val_loss: 0.5596 - val_acc: 0.7209 Epoch 2/100 390870/390870 [==============================] - 132s 339us/step - loss: 0.5530 - acc: 0.7257 - val_loss: 0.5444 - val_acc: 0.7335 Epoch 3/100 390870/390870 [==============================] - 92s 235us/step - loss: 0.5437 - acc: 0.7323 - val_loss: 0.5402 - val_acc: 0.7374 Epoch 4/100 390870/390870 [==============================] - 80s 205us/step - loss: 0.5398 - acc: 0.7353 - val_loss: 0.5386 - val_acc: 0.7420 Epoch 5/100 390870/390870 [==============================] - 76s 194us/step - loss: 0.5376 - acc: 0.7369 - val_loss: 0.5458 - val_acc: 0.7319 Epoch 6/100 390870/390870 [==============================] - 78s 198us/step - loss: 0.5349 - acc: 0.7388 - val_loss: 0.5438 - val_acc: 0.7389 Epoch 7/100 390870/390870 [==============================] - 76s 195us/step - loss: 0.5334 - acc: 0.7397 - val_loss: 0.5317 - val_acc: 0.7416 Epoch 8/100 390870/390870 [==============================] - 74s 190us/step - loss: 0.5311 - acc: 0.7415 - val_loss: 0.5345 - val_acc: 0.7405 Epoch 9/100 390870/390870 [==============================] - 76s 195us/step - loss: 0.5299 - acc: 0.7424 - val_loss: 0.5317 - val_acc: 0.7461 Epoch 10/100 390870/390870 [==============================] - 81s 206us/step - loss: 0.5287 - acc: 0.7425 - val_loss: 0.5283 - val_acc: 0.7460 Epoch 11/100 390870/390870 [==============================] - 95s 244us/step - loss: 0.5276 - acc: 0.7439 - val_loss: 0.5367 - val_acc: 0.7427 Epoch 12/100 390870/390870 [==============================] - 78s 199us/step - loss: 0.5263 - acc: 0.7445 - val_loss: 0.5266 - val_acc: 0.7483 Epoch 13/100 390870/390870 [==============================] - 75s 193us/step - loss: 0.5253 - acc: 0.7453 - val_loss: 0.5278 - val_acc: 0.7463 Epoch 14/100 390870/390870 [==============================] - 76s 195us/step - loss: 0.5242 - acc: 0.7459 - val_loss: 0.5301 - val_acc: 0.7438 Epoch 15/100 390870/390870 [==============================] - 80s 206us/step - loss: 0.5239 - acc: 0.7464 - val_loss: 0.5226 - val_acc: 0.7489 Epoch 16/100 390870/390870 [==============================] - 74s 188us/step - loss: 0.5224 - acc: 0.7475 - val_loss: 0.5273 - val_acc: 0.7468 Epoch 17/100 390870/390870 [==============================] - 74s 190us/step - loss: 0.5222 - acc: 0.7471 - val_loss: 0.5276 - val_acc: 0.7452 Epoch 18/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5216 - acc: 0.7483 - val_loss: 0.5333 - val_acc: 0.7430 Epoch 19/100 390870/390870 [==============================] - 72s 185us/step - loss: 0.5208 - acc: 0.7486 - val_loss: 0.5300 - val_acc: 0.7431 Epoch 20/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5201 - acc: 0.7489 - val_loss: 0.5244 - val_acc: 0.7486 Epoch 21/100 390870/390870 [==============================] - 72s 185us/step - loss: 0.5198 - acc: 0.7493 - val_loss: 0.5246 - val_acc: 0.7478 Epoch 22/100 390870/390870 [==============================] - 73s 186us/step - loss: 0.5195 - acc: 0.7493 - val_loss: 0.5225 - val_acc: 0.7476 Epoch 23/100 390870/390870 [==============================] - 72s 185us/step - loss: 0.5189 - acc: 0.7499 - val_loss: 0.5311 - val_acc: 0.7405 Epoch 24/100 390870/390870 [==============================] - 74s 189us/step - loss: 0.5188 - acc: 0.7495 - val_loss: 0.5260 - val_acc: 0.7463 Epoch 25/100 390870/390870 [==============================] - 74s 190us/step - loss: 0.5180 - acc: 0.7506 - val_loss: 0.5273 - val_acc: 0.7459 Epoch 26/100 390870/390870 [==============================] - 73s 187us/step - loss: 0.5176 - acc: 0.7511 - val_loss: 0.5262 - val_acc: 0.7446 Epoch 27/100 390870/390870 [==============================] - 73s 186us/step - loss: 0.5171 - acc: 0.7505 - val_loss: 0.5367 - val_acc: 0.7423 Epoch 28/100 390870/390870 [==============================] - 92s 236us/step - loss: 0.5163 - acc: 0.7516 - val_loss: 0.5235 - val_acc: 0.7469 Epoch 29/100 390870/390870 [==============================] - 78s 198us/step - loss: 0.5163 - acc: 0.7515 - val_loss: 0.5213 - val_acc: 0.7492 Epoch 30/100 390870/390870 [==============================] - 76s 195us/step - loss: 0.5162 - acc: 0.7514 - val_loss: 0.5226 - val_acc: 0.7480 Epoch 31/100 390870/390870 [==============================] - 72s 185us/step - loss: 0.5155 - acc: 0.7518 - val_loss: 0.5293 - val_acc: 0.7445 Epoch 32/100 390870/390870 [==============================] - 73s 187us/step - loss: 0.5146 - acc: 0.7524 - val_loss: 0.5318 - val_acc: 0.7457 Epoch 33/100 390870/390870 [==============================] - 73s 186us/step - loss: 0.5144 - acc: 0.7521 - val_loss: 0.5209 - val_acc: 0.7499 Epoch 34/100 390870/390870 [==============================] - 72s 184us/step - loss: 0.5143 - acc: 0.7529 - val_loss: 0.5211 - val_acc: 0.7481 Epoch 35/100 390870/390870 [==============================] - 73s 185us/step - loss: 0.5134 - acc: 0.7530 - val_loss: 0.5218 - val_acc: 0.7470 Epoch 36/100 390870/390870 [==============================] - 75s 193us/step - loss: 0.5132 - acc: 0.7532 - val_loss: 0.5192 - val_acc: 0.7500 Epoch 37/100 390870/390870 [==============================] - 77s 196us/step - loss: 0.5128 - acc: 0.7533 - val_loss: 0.5198 - val_acc: 0.7502 Epoch 38/100 390870/390870 [==============================] - 77s 197us/step - loss: 0.5133 - acc: 0.7531 - val_loss: 0.5205 - val_acc: 0.7489 Epoch 39/100 390870/390870 [==============================] - 77s 197us/step - loss: 0.5127 - acc: 0.7534 - val_loss: 0.5202 - val_acc: 0.7491 Epoch 40/100 390870/390870 [==============================] - 82s 210us/step - loss: 0.5122 - acc: 0.7536 - val_loss: 0.5198 - val_acc: 0.7494 Epoch 41/100 390870/390870 [==============================] - 77s 197us/step - loss: 0.5120 - acc: 0.7536 - val_loss: 0.5220 - val_acc: 0.7478 Epoch 42/100 390870/390870 [==============================] - 76s 195us/step - loss: 0.5120 - acc: 0.7537 - val_loss: 0.5203 - val_acc: 0.7497 Epoch 43/100 390870/390870 [==============================] - 77s 196us/step - loss: 0.5117 - acc: 0.7541 - val_loss: 0.5228 - val_acc: 0.7490 Epoch 44/100 390870/390870 [==============================] - 74s 188us/step - loss: 0.5109 - acc: 0.7545 - val_loss: 0.5198 - val_acc: 0.7495 Epoch 45/100 390870/390870 [==============================] - 73s 186us/step - loss: 0.5139 - acc: 0.7526 - val_loss: 0.5190 - val_acc: 0.7505 Epoch 46/100 390870/390870 [==============================] - 73s 188us/step - loss: 0.5104 - acc: 0.7548 - val_loss: 0.5273 - val_acc: 0.7465 Epoch 47/100 390870/390870 [==============================] - 73s 187us/step - loss: 0.5104 - acc: 0.7545 - val_loss: 0.5218 - val_acc: 0.7480 Epoch 48/100 390870/390870 [==============================] - 79s 202us/step - loss: 0.5100 - acc: 0.7545 - val_loss: 0.5193 - val_acc: 0.7506 Epoch 49/100 390870/390870 [==============================] - 78s 200us/step - loss: 0.5098 - acc: 0.7549 - val_loss: 0.5275 - val_acc: 0.7479 Epoch 50/100 390870/390870 [==============================] - 90s 230us/step - loss: 0.5096 - acc: 0.7550 - val_loss: 0.5255 - val_acc: 0.7474 Epoch 51/100 390870/390870 [==============================] - 100s 255us/step - loss: 0.5093 - acc: 0.7553 - val_loss: 0.5207 - val_acc: 0.7522 Epoch 52/100 390870/390870 [==============================] - 79s 201us/step - loss: 0.5093 - acc: 0.7549 - val_loss: 0.5196 - val_acc: 0.7514 Epoch 53/100 390870/390870 [==============================] - 90s 230us/step - loss: 0.5086 - acc: 0.7554 - val_loss: 0.5198 - val_acc: 0.7516 Epoch 54/100 390870/390870 [==============================] - 91s 232us/step - loss: 0.5092 - acc: 0.7558 - val_loss: 0.5237 - val_acc: 0.7477 Epoch 55/100 390870/390870 [==============================] - 103s 265us/step - loss: 0.5085 - acc: 0.7556 - val_loss: 0.5259 - val_acc: 0.7481 Epoch 56/100 390870/390870 [==============================] - 112s 287us/step - loss: 0.5084 - acc: 0.7555 - val_loss: 0.5217 - val_acc: 0.7498 Epoch 57/100 390870/390870 [==============================] - 94s 241us/step - loss: 0.5083 - acc: 0.7555 - val_loss: 0.5194 - val_acc: 0.7516 Epoch 58/100 390870/390870 [==============================] - 89s 228us/step - loss: 0.5079 - acc: 0.7563 - val_loss: 0.5220 - val_acc: 0.7506 Epoch 59/100 390870/390870 [==============================] - 82s 210us/step - loss: 0.5080 - acc: 0.7557 - val_loss: 0.5229 - val_acc: 0.7503 Epoch 60/100 390870/390870 [==============================] - 77s 196us/step - loss: 0.5073 - acc: 0.7559 - val_loss: 0.5187 - val_acc: 0.7518 Epoch 61/100 390870/390870 [==============================] - 77s 197us/step - loss: 0.5070 - acc: 0.7566 - val_loss: 0.5272 - val_acc: 0.7465 Epoch 62/100 390870/390870 [==============================] - 85s 217us/step - loss: 0.5070 - acc: 0.7570 - val_loss: 0.5198 - val_acc: 0.7506 Epoch 63/100 390870/390870 [==============================] - 101s 258us/step - loss: 0.5067 - acc: 0.7566 - val_loss: 0.5224 - val_acc: 0.7503 Epoch 64/100 390870/390870 [==============================] - 113s 289us/step - loss: 0.5063 - acc: 0.7567 - val_loss: 0.5261 - val_acc: 0.7476 Epoch 65/100 390870/390870 [==============================] - 117s 299us/step - loss: 0.5071 - acc: 0.7564 - val_loss: 0.5200 - val_acc: 0.7499 Epoch 66/100 390870/390870 [==============================] - 111s 285us/step - loss: 0.5063 - acc: 0.7565 - val_loss: 0.5235 - val_acc: 0.7477 Epoch 67/100 390870/390870 [==============================] - 88s 226us/step - loss: 0.5065 - acc: 0.7561 - val_loss: 0.5248 - val_acc: 0.7497 Epoch 68/100 390870/390870 [==============================] - 89s 228us/step - loss: 0.5064 - acc: 0.7562 - val_loss: 0.5201 - val_acc: 0.7511 Epoch 69/100 390870/390870 [==============================] - 82s 209us/step - loss: 0.5064 - acc: 0.7568 - val_loss: 0.5265 - val_acc: 0.7486 Epoch 70/100 390870/390870 [==============================] - 82s 209us/step - loss: 0.5062 - acc: 0.7570 - val_loss: 0.5222 - val_acc: 0.7506 Epoch 71/100 390870/390870 [==============================] - 80s 206us/step - loss: 0.5058 - acc: 0.7564 - val_loss: 0.5197 - val_acc: 0.7507 Epoch 72/100 390870/390870 [==============================] - 84s 214us/step - loss: 0.5055 - acc: 0.7573 - val_loss: 0.5231 - val_acc: 0.7489 Epoch 73/100 390870/390870 [==============================] - 95s 244us/step - loss: 0.5052 - acc: 0.7573 - val_loss: 0.5216 - val_acc: 0.7492 Epoch 74/100 390870/390870 [==============================] - 100s 257us/step - loss: 0.5052 - acc: 0.7576 - val_loss: 0.5250 - val_acc: 0.7475 Epoch 75/100 390870/390870 [==============================] - 99s 253us/step - loss: 0.5055 - acc: 0.7571 - val_loss: 0.5184 - val_acc: 0.7519 Epoch 76/100 390870/390870 [==============================] - 84s 215us/step - loss: 0.5047 - acc: 0.7576 - val_loss: 0.5311 - val_acc: 0.7466 Epoch 77/100 390870/390870 [==============================] - 87s 223us/step - loss: 0.5048 - acc: 0.7577 - val_loss: 0.5252 - val_acc: 0.7476 Epoch 78/100 390870/390870 [==============================] - 84s 215us/step - loss: 0.5054 - acc: 0.7566 - val_loss: 0.5271 - val_acc: 0.7445 Epoch 79/100 390870/390870 [==============================] - 101s 259us/step - loss: 0.5045 - acc: 0.7573 - val_loss: 0.5220 - val_acc: 0.7510 Epoch 80/100 390870/390870 [==============================] - 107s 274us/step - loss: 0.5043 - acc: 0.7575 - val_loss: 0.5237 - val_acc: 0.7477 Epoch 81/100 390870/390870 [==============================] - 98s 250us/step - loss: 0.5042 - acc: 0.7577 - val_loss: 0.5262 - val_acc: 0.7489 Epoch 82/100 390870/390870 [==============================] - 101s 259us/step - loss: 0.5048 - acc: 0.7574 - val_loss: 0.5237 - val_acc: 0.7488 Epoch 83/100 390870/390870 [==============================] - 100s 256us/step - loss: 0.5037 - acc: 0.7581 - val_loss: 0.5210 - val_acc: 0.7500 Epoch 84/100 390870/390870 [==============================] - 82s 209us/step - loss: 0.5040 - acc: 0.7577 - val_loss: 0.5231 - val_acc: 0.7492 Epoch 85/100 390870/390870 [==============================] - 90s 230us/step - loss: 0.5039 - acc: 0.7577 - val_loss: 0.5263 - val_acc: 0.7485 Epoch 86/100 390870/390870 [==============================] - 81s 208us/step - loss: 0.5040 - acc: 0.7578 - val_loss: 0.5234 - val_acc: 0.7503 Epoch 87/100 390870/390870 [==============================] - 82s 211us/step - loss: 0.5032 - acc: 0.7582 - val_loss: 0.5262 - val_acc: 0.7494 Epoch 88/100 390870/390870 [==============================] - 84s 214us/step - loss: 0.5031 - acc: 0.7586 - val_loss: 0.5333 - val_acc: 0.7450 Epoch 89/100 390870/390870 [==============================] - 91s 233us/step - loss: 0.5028 - acc: 0.7583 - val_loss: 0.5206 - val_acc: 0.7496 Epoch 90/100 390870/390870 [==============================] - 87s 223us/step - loss: 0.5024 - acc: 0.7590 - val_loss: 0.5294 - val_acc: 0.7455 Epoch 91/100 390870/390870 [==============================] - 87s 223us/step - loss: 0.5033 - acc: 0.7585 - val_loss: 0.5391 - val_acc: 0.7455 Epoch 92/100 390870/390870 [==============================] - 87s 223us/step - loss: 0.5028 - acc: 0.7586 - val_loss: 0.5250 - val_acc: 0.7494 Epoch 93/100 390870/390870 [==============================] - 92s 234us/step - loss: 0.5032 - acc: 0.7589 - val_loss: 0.5213 - val_acc: 0.7506 Epoch 94/100 390870/390870 [==============================] - 87s 222us/step - loss: 0.5024 - acc: 0.7588 - val_loss: 0.5243 - val_acc: 0.7493 Epoch 95/100 390870/390870 [==============================] - 84s 216us/step - loss: 0.5023 - acc: 0.7582 - val_loss: 0.5226 - val_acc: 0.7497 Epoch 96/100 390870/390870 [==============================] - 86s 220us/step - loss: 0.5026 - acc: 0.7583 - val_loss: 0.5276 - val_acc: 0.7455 Epoch 97/100 390870/390870 [==============================] - 82s 209us/step - loss: 0.5020 - acc: 0.7587 - val_loss: 0.5239 - val_acc: 0.7504 Epoch 98/100 390870/390870 [==============================] - 78s 200us/step - loss: 0.5022 - acc: 0.7591 - val_loss: 0.5307 - val_acc: 0.7451 Epoch 99/100 390870/390870 [==============================] - 74s 190us/step - loss: 0.5018 - acc: 0.7589 - val_loss: 0.5308 - val_acc: 0.7447 Epoch 100/100 390870/390870 [==============================] - 87s 222us/step - loss: 0.5023 - acc: 0.7592 - val_loss: 0.5284 - val_acc: 0.7470
<keras.callbacks.History at 0x19a25de07b8>
# Save trained model
classifier.save("../models/trained_deep_neural_network_all9.h5")
# evaluation of the model training
classifier = _load_model("../models/trained_deep_neural_network_all8.h5") # DEBUG!!!
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 58s 104us/step [0.5078941215270898, 0.7580132739713417]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_2.pickle')
test_set.shape
(153993, 2774)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[0.],
[0.],
[1.],
...,
[1.],
[1.],
[1.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.67 0.44 0.53 50930
1 0.76 0.89 0.82 103063
accuracy 0.74 153993
macro avg 0.72 0.67 0.68 153993
weighted avg 0.73 0.74 0.73 153993
Accuracy for Deep Learning approach: 74.25792081458248
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[22257 28673] [10968 92095]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set[train_set['bin_truth_score']!=-1]
train_set.shape
(438571, 787)
best_model = _jl.load("../models/best_SVM.joblib")
best_model.set_params(verbose=10)
best_model.get_params()
{'C': 0.001,
'class_weight': None,
'dual': True,
'fit_intercept': True,
'intercept_scaling': 1,
'loss': 'squared_hinge',
'max_iter': 50000,
'multi_class': 'ovr',
'penalty': 'l2',
'random_state': 0,
'tol': 0.0001,
'verbose': 10}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[LibLinear]
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=50000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=10)
_jl.dump(best_model, "../models/best_SVM_fake.joblib")
['../models/best_SVM_fake.joblib']
print("coef:", best_model.coef_)
print("intercept:", best_model.intercept_)
coef: [[-1.38515487e-01 -1.96857288e-01 3.32402060e-01 -2.91255272e-01 -1.69144522e-01 4.39209749e-02 2.42195943e-02 3.64156997e-02 -2.46790916e-02 -1.02787607e-02 -4.86873857e-02 -4.97584956e-05 -3.22987927e-02 6.88759948e-03 -1.03371270e-05 6.50742395e-01 -1.29562758e-04 1.03416295e-03 -2.48775106e-04 -6.59642374e-04 -1.02036028e-04 -9.66791010e-04 1.14359591e-03 -1.99843890e-04 -6.27763021e-05 3.69227212e-04 2.38756561e-06 -1.18488072e-05 3.94093254e-05 -2.29569450e-04 -8.67235837e-02 8.89035480e-06 -7.57976137e-04 3.93742819e-04 -4.55403368e-02 -3.54663199e-04 -3.18629560e-02 5.23979128e-02 3.66236285e-04 1.77877828e-02 -8.52227099e-04 -2.36155968e-04 9.56668382e-05 1.75930155e-04 6.77026802e-05 -5.13757145e-03 1.89471500e-02 -8.09871718e-04 3.60054753e-03 6.19090568e-03 3.79168413e-02 -8.57468383e-04 5.00425524e-02 5.05812751e-03 2.68403245e-02 4.03692064e-02 2.11274126e-02 2.88136834e-02 1.73991590e-02 -5.80037087e-03 -6.35825432e-03 -1.38553686e-03 3.66697695e-03 4.09770364e-03 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2.05874551e-02 -4.80607788e-03 -1.86829790e-02 -1.45709490e-02 -1.06623722e-02 2.35326667e-02 -4.70131386e-03 -4.21315624e-03 -2.71234831e-03 1.93383614e-03 -1.89008818e-02 4.86325848e-03 5.09044025e-02 7.89412493e-03 -1.85020881e-02 -1.65166310e-02 2.59124645e-02 -3.96222717e-03 -1.82581455e-03 -2.84639480e-03 -6.21755254e-04 5.29924476e-03 -1.50841079e-02 -4.39750044e-03 -4.42544188e-03 1.09647180e-02 -9.85470723e-03 1.74375033e-02 4.02869950e-03 -9.80037647e-03 4.82269799e-03 -5.86701112e-02 -9.05448823e-03 3.29424010e-02 -3.10830934e-02 -4.07787452e-02 1.32919262e-02 6.53352095e-02 -5.48590234e-03 1.02267075e-02 6.69731008e-03 2.74703725e-02 1.88225893e-02 6.64172161e-02 9.56234199e-03 5.69613301e-03 1.00842961e-02 2.02640525e-03 4.17134666e-03 8.80238582e-04 -9.58839652e-03 -1.12144824e-02 -1.68011935e-02 1.76108325e-02 -2.49569806e-02 2.10639397e-02 1.62382271e-02 -9.51530479e-03 3.35571782e-02 8.82414610e-04 -6.01971158e-03 1.40010253e-02 -2.23258493e-03 1.09818752e-02 -1.32184042e-02 -1.16853055e-02 7.75300062e-03 -2.26713625e-03 2.36595861e-02 -2.33222683e-02 -5.46096270e-02 4.16589791e-02 -1.03794442e-03 -3.14466420e-02 2.01603301e-02 -2.96612174e-02 5.00838207e-03 -2.72989050e-02 2.37512453e-02 -1.19723609e-02 -1.16283640e-03 1.77372095e-02 -1.47336557e-02 1.31102188e-03 8.51543272e-03 -6.68587037e-03 1.99759053e-02 -3.03666952e-02 -1.59449661e-02 -7.98116436e-03 6.09597181e-03 3.07038472e-02 1.60983762e-03 -2.55458962e-02 2.57303489e-02 3.98623009e-03 -2.49897178e-03 -4.61094740e-02 3.68646964e-02 6.88660748e-03 1.87634909e-02 7.88533428e-03 -3.23301450e-03 -5.52813662e-03 -1.78606036e-02 -1.79463803e-02 -3.57198186e-02 2.53094829e-02 3.93922796e-03 1.61779464e-02 1.90456601e-02 2.49921754e-02 1.21722342e-02 1.34071085e-03 1.45581362e-02 -2.14437115e-03 2.24086577e-02 -4.53714331e-02 -2.74531767e-03 -3.40563217e-03 -1.26440357e-02 -3.12196341e-02 1.86247094e-02 -3.28087898e-03 1.82956600e-03 -3.64854486e-02 1.26395711e-02 4.91543431e-03 1.50202129e-03 -4.86245017e-03 1.39062633e-03 1.69879728e-03 7.54090643e-03 -1.10152787e-02 -4.04248367e-03 -7.24537298e-04 -4.03502454e-02 -3.46059332e-02 -7.92174591e-04 -2.78487445e-02 4.22124460e-03 -2.34118625e-03 4.68893202e-02 -2.40964309e-02 6.58263426e-03 0.00000000e+00 -1.06404899e-02 -6.57115036e-04 1.83736987e-03 4.71624881e-03 4.59326667e-03 -2.11296436e-02 7.69920334e-03 9.63588835e-03 6.97926589e-03 -1.31042500e-02 5.12822543e-04 1.13357609e-02 7.51542321e-04 -2.96353118e-02 -1.27433769e-02 4.35726615e-03 6.12298723e-04 -2.87901293e-02 8.37917698e-03 4.15168647e-03 6.05821477e-03 1.69256363e-02 -3.96513268e-02 8.41897903e-03 2.59189050e-02 -2.40119082e-02 3.51413642e-02 -1.20591670e-02 4.02558898e-03 3.64187455e-02 -2.15658178e-02 -8.83495685e-03 -9.75622677e-05 -1.07509087e-02 5.12346974e-03 -3.10714314e-02 -9.04470855e-03 -1.59977282e-02 -1.46438349e-02 -1.78883469e-02 -1.92557780e-02 1.78333800e-03 -1.03883290e-03 9.93805978e-04 -2.24903444e-02 1.16720118e-07 5.36833867e-02 1.95375589e-03 -2.11893072e-02 1.07674429e-02 -5.14852512e-03 -5.69404369e-02 -4.48608520e-02 7.93865362e-02 1.16369602e-02 8.60992692e-03 4.13415067e-03 -2.38109424e-02 -1.16553406e-02 2.12244496e-03 -3.24198574e-02 1.85734204e-02 -6.67553656e-03 0.00000000e+00 8.93218329e-03 -3.78716311e-02 -6.08784528e-03 -2.19712782e-02 3.89818898e-03 5.78575117e-03 2.84085929e-03 -1.11183069e-02 4.26315805e-03 -5.78328837e-03 -1.86083811e-02 -9.31472045e-03 1.71179464e-02 2.78352707e-03 -5.24710885e-03 -1.58906177e-02 -2.57995935e-02 -3.54469438e-03 -3.04936422e-03 -2.23340727e-02 -2.05275732e-03 -1.51540270e-03 -2.89039785e-03 1.35720572e-02 5.00110540e-03 -2.03782712e-03 1.31302093e-02 3.12155947e-03 9.66232590e-03 -3.82160421e-03 1.59629542e-02 -3.13063947e-02 -7.70711547e-02 -4.53873213e-02 2.44396713e-02 1.16176026e-02 -4.28616324e-03 2.93571138e-03 -3.21378840e-03 -5.05168012e-03 2.14775046e-02 9.30468055e-03 1.07742979e-02 1.09680076e-02 3.36540862e-03 1.01536295e-02 2.41527039e-03 -5.70225641e-03 1.03883289e-02 1.02343923e-03 1.32746365e-02 -8.20576467e-04 -6.66446767e-03 7.90373582e-04 7.65092311e-03 1.32660069e-02 2.26848794e-02 2.28302296e-03 -1.40102265e-02 -3.71336568e-03 2.64946628e-02 -1.79877478e-02 1.81385643e-02 3.41749975e-02 -3.40065409e-03 5.02072962e-03 -8.78754424e-04 -2.26040006e-03 1.29404119e-03 -5.50349194e-03 2.05685501e-03 -2.00369264e-03 9.98966381e-03 -3.18727710e-02 3.82714327e-05 3.85631484e-04 1.10011642e-02 -2.09306138e-02 -1.51879536e-02 -1.86591424e-02 -4.32213442e-02 1.26460455e-02 2.51820690e-02 7.44768353e-03 2.62360977e-02 -3.11786477e-03 1.65680606e-02 -1.20075957e-02 -2.05131750e-03 5.09738736e-03 3.23257670e-02 -3.96595964e-03 1.85930483e-02 -2.01630691e-02 1.19437868e-02 1.96814717e-03 1.59577917e-02 -1.34377019e-02 3.74845529e-04 1.97689620e-02 -8.72276622e-04 2.34626536e-03 -2.57764641e-02 -1.16449587e-02 1.23092515e-02 3.96490754e-02 -3.16921730e-02 -2.32013547e-02 6.75377040e-04 -6.54763055e-04 9.46946754e-03 5.48372418e-04 7.70140168e-03 -4.63439632e-02 -7.70876405e-04 2.54325016e-02 2.66350587e-02 3.31780889e-02 0.00000000e+00 -1.35134870e-02 6.09026263e-04 3.24610758e-03 -4.83750860e-03]] intercept: [-0.29125527]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set.shape
(153993, 787)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
predictions: [0 1 0 ... 0 1 1]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Support Vector Machine:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Support Vector Machine:", _accuracy_score(test_set['likes'], predic)*100)
Report for Support Vector Machine:
precision recall f1-score support
0 0.65 0.35 0.45 50930
1 0.74 0.91 0.81 103063
accuracy 0.72 153993
macro avg 0.70 0.63 0.63 153993
weighted avg 0.71 0.72 0.70 153993
Accuracy for Support Vector Machine: 72.28900014935743
# Confusion matrix for SVC
print("Confusion Matrix for SVC: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for SVC:
array([[17698, 33232],
[ 9441, 93622]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("SVM ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set[train_set['bin_truth_score']!=-1]
train_set.shape
(438571, 787)
params = _jl.load("../models/best_Random_Forest_2.joblib").get_params()
params['n_jobs'] = -1
params['verbose'] = 10
best_model = _RandomForestClassifier(**params)
best_model.get_params()
{'bootstrap': False,
'class_weight': None,
'criterion': 'entropy',
'max_depth': 50,
'max_features': 'auto',
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_impurity_split': None,
'min_samples_leaf': 2,
'min_samples_split': 10,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 1000,
'n_jobs': -1,
'oob_score': False,
'random_state': None,
'verbose': 10,
'warm_start': False}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.
building tree 1 of 1000 building tree 2 of 1000 building tree 3 of 1000 building tree 4 of 1000 building tree 5 of 1000 building tree 6 of 1000 building tree 7 of 1000 building tree 8 of 1000 building tree 9 of 1000 building tree 10 of 1000 building tree 11 of 1000 building tree 12 of 1000 building tree 13 of 1000
[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 16.4s
building tree 14 of 1000 building tree 15 of 1000 building tree 16 of 1000 building tree 17 of 1000 building tree 18 of 1000 building tree 19 of 1000 building tree 20 of 1000 building tree 21 of 1000
[Parallel(n_jobs=-1)]: Done 8 tasks | elapsed: 20.2s
building tree 22 of 1000 building tree 23 of 1000 building tree 24 of 1000 building tree 25 of 1000 building tree 26 of 1000 building tree 27 of 1000 building tree 28 of 1000
[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 37.7s
building tree 29 of 1000 building tree 30 of 1000 building tree 31 of 1000 building tree 32 of 1000 building tree 33 of 1000 building tree 34 of 1000 building tree 35 of 1000 building tree 36 of 1000 building tree 37 of 1000
[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 55.7s
building tree 38 of 1000 building tree 39 of 1000 building tree 40 of 1000 building tree 41 of 1000 building tree 42 of 1000 building tree 43 of 1000 building tree 44 of 1000 building tree 45 of 1000 building tree 46 of 1000 building tree 47 of 1000 building tree 48 of 1000 building tree 49 of 1000 building tree 50 of 1000
[Parallel(n_jobs=-1)]: Done 37 tasks | elapsed: 1.2min
building tree 51 of 1000 building tree 52 of 1000 building tree 53 of 1000 building tree 54 of 1000 building tree 55 of 1000 building tree 56 of 1000 building tree 57 of 1000 building tree 58 of 1000 building tree 59 of 1000
[Parallel(n_jobs=-1)]: Done 48 tasks | elapsed: 1.3min
building tree 60 of 1000 building tree 61 of 1000 building tree 62 of 1000 building tree 63 of 1000 building tree 64 of 1000 building tree 65 of 1000 building tree 66 of 1000 building tree 67 of 1000 building tree 68 of 1000 building tree 69 of 1000 building tree 70 of 1000 building tree 71 of 1000 building tree 72 of 1000
[Parallel(n_jobs=-1)]: Done 61 tasks | elapsed: 1.9min
building tree 73 of 1000 building tree 74 of 1000 building tree 75 of 1000 building tree 76 of 1000 building tree 77 of 1000 building tree 78 of 1000 building tree 79 of 1000 building tree 80 of 1000 building tree 81 of 1000 building tree 82 of 1000 building tree 83 of 1000 building tree 84 of 1000 building tree 85 of 1000 building tree 86 of 1000
[Parallel(n_jobs=-1)]: Done 74 tasks | elapsed: 2.2min
building tree 87 of 1000 building tree 88 of 1000 building tree 89 of 1000 building tree 90 of 1000 building tree 91 of 1000 building tree 92 of 1000 building tree 93 of 1000 building tree 94 of 1000 building tree 95 of 1000 building tree 96 of 1000 building tree 97 of 1000 building tree 98 of 1000 building tree 99 of 1000 building tree 100 of 1000
[Parallel(n_jobs=-1)]: Done 89 tasks | elapsed: 2.5min
building tree 101 of 1000 building tree 102 of 1000 building tree 103 of 1000 building tree 104 of 1000 building tree 105 of 1000 building tree 106 of 1000 building tree 107 of 1000 building tree 108 of 1000 building tree 109 of 1000 building tree 110 of 1000 building tree 111 of 1000 building tree 112 of 1000 building tree 113 of 1000 building tree 114 of 1000building tree 115 of 1000 building tree 116 of 1000
[Parallel(n_jobs=-1)]: Done 104 tasks | elapsed: 2.9min
building tree 117 of 1000 building tree 118 of 1000 building tree 119 of 1000 building tree 120 of 1000 building tree 121 of 1000 building tree 122 of 1000 building tree 123 of 1000 building tree 124 of 1000 building tree 125 of 1000 building tree 126 of 1000 building tree 127 of 1000 building tree 128 of 1000 building tree 129 of 1000 building tree 130 of 1000 building tree 131 of 1000 building tree 132 of 1000
[Parallel(n_jobs=-1)]: Done 121 tasks | elapsed: 3.4min
building tree 133 of 1000 building tree 134 of 1000 building tree 135 of 1000 building tree 136 of 1000 building tree 137 of 1000 building tree 138 of 1000 building tree 139 of 1000 building tree 140 of 1000 building tree 141 of 1000 building tree 142 of 1000 building tree 143 of 1000 building tree 144 of 1000 building tree 145 of 1000 building tree 146 of 1000 building tree 147 of 1000 building tree 148 of 1000 building tree 149 of 1000
[Parallel(n_jobs=-1)]: Done 138 tasks | elapsed: 3.9min
building tree 150 of 1000 building tree 151 of 1000 building tree 152 of 1000 building tree 153 of 1000 building tree 154 of 1000 building tree 155 of 1000 building tree 156 of 1000 building tree 157 of 1000 building tree 158 of 1000 building tree 159 of 1000 building tree 160 of 1000 building tree 161 of 1000 building tree 162 of 1000 building tree 163 of 1000 building tree 164 of 1000 building tree 165 of 1000 building tree 166 of 1000 building tree 167 of 1000 building tree 168 of 1000
[Parallel(n_jobs=-1)]: Done 157 tasks | elapsed: 4.4min
building tree 169 of 1000 building tree 170 of 1000 building tree 171 of 1000 building tree 172 of 1000 building tree 173 of 1000 building tree 174 of 1000 building tree 175 of 1000 building tree 176 of 1000 building tree 177 of 1000 building tree 178 of 1000 building tree 179 of 1000 building tree 180 of 1000 building tree 181 of 1000 building tree 182 of 1000 building tree 183 of 1000 building tree 184 of 1000 building tree 185 of 1000 building tree 186 of 1000 building tree 187 of 1000
[Parallel(n_jobs=-1)]: Done 176 tasks | elapsed: 4.9min
building tree 188 of 1000 building tree 189 of 1000 building tree 190 of 1000 building tree 191 of 1000 building tree 192 of 1000 building tree 193 of 1000 building tree 194 of 1000 building tree 195 of 1000 building tree 196 of 1000 building tree 197 of 1000 building tree 198 of 1000 building tree 199 of 1000 building tree 200 of 1000 building tree 201 of 1000 building tree 202 of 1000 building tree 203 of 1000 building tree 204 of 1000 building tree 205 of 1000 building tree 206 of 1000 building tree 207 of 1000 building tree 208 of 1000
[Parallel(n_jobs=-1)]: Done 197 tasks | elapsed: 5.5min
building tree 209 of 1000 building tree 210 of 1000 building tree 211 of 1000 building tree 212 of 1000 building tree 213 of 1000 building tree 214 of 1000 building tree 215 of 1000 building tree 216 of 1000 building tree 217 of 1000 building tree 218 of 1000 building tree 219 of 1000 building tree 220 of 1000 building tree 221 of 1000 building tree 222 of 1000 building tree 223 of 1000 building tree 224 of 1000 building tree 225 of 1000 building tree 226 of 1000 building tree 227 of 1000 building tree 228 of 1000 building tree 229 of 1000
[Parallel(n_jobs=-1)]: Done 218 tasks | elapsed: 6.1min
building tree 230 of 1000 building tree 231 of 1000 building tree 232 of 1000 building tree 233 of 1000 building tree 234 of 1000 building tree 235 of 1000 building tree 236 of 1000 building tree 237 of 1000 building tree 238 of 1000 building tree 239 of 1000 building tree 240 of 1000 building tree 241 of 1000 building tree 242 of 1000 building tree 243 of 1000 building tree 244 of 1000 building tree 245 of 1000 building tree 246 of 1000 building tree 247 of 1000 building tree 248 of 1000 building tree 249 of 1000 building tree 250 of 1000 building tree 251 of 1000 building tree 252 of 1000
[Parallel(n_jobs=-1)]: Done 241 tasks | elapsed: 6.8min
building tree 253 of 1000 building tree 254 of 1000 building tree 255 of 1000 building tree 256 of 1000 building tree 257 of 1000 building tree 258 of 1000 building tree 259 of 1000 building tree 260 of 1000 building tree 261 of 1000 building tree 262 of 1000 building tree 263 of 1000 building tree 264 of 1000 building tree 265 of 1000 building tree 266 of 1000 building tree 267 of 1000 building tree 268 of 1000 building tree 269 of 1000 building tree 270 of 1000 building tree 271 of 1000 building tree 272 of 1000 building tree 273 of 1000 building tree 274 of 1000 building tree 275 of 1000
[Parallel(n_jobs=-1)]: Done 264 tasks | elapsed: 7.3min
building tree 276 of 1000 building tree 277 of 1000 building tree 278 of 1000 building tree 279 of 1000 building tree 280 of 1000 building tree 281 of 1000 building tree 282 of 1000 building tree 283 of 1000 building tree 284 of 1000 building tree 285 of 1000 building tree 286 of 1000 building tree 287 of 1000 building tree 288 of 1000 building tree 289 of 1000 building tree 290 of 1000 building tree 291 of 1000 building tree 292 of 1000 building tree 293 of 1000 building tree 294 of 1000 building tree 295 of 1000 building tree 296 of 1000 building tree 297 of 1000 building tree 298 of 1000 building tree 299 of 1000 building tree 300 of 1000 building tree 301 of 1000
[Parallel(n_jobs=-1)]: Done 289 tasks | elapsed: 8.1min
building tree 302 of 1000 building tree 303 of 1000 building tree 304 of 1000 building tree 305 of 1000 building tree 306 of 1000 building tree 307 of 1000 building tree 308 of 1000 building tree 309 of 1000 building tree 310 of 1000 building tree 311 of 1000 building tree 312 of 1000 building tree 313 of 1000 building tree 314 of 1000 building tree 315 of 1000 building tree 316 of 1000 building tree 317 of 1000 building tree 318 of 1000 building tree 319 of 1000 building tree 320 of 1000 building tree 321 of 1000 building tree 322 of 1000 building tree 323 of 1000 building tree 324 of 1000 building tree 325 of 1000 building tree 326 of 1000
[Parallel(n_jobs=-1)]: Done 314 tasks | elapsed: 8.7min
building tree 327 of 1000 building tree 328 of 1000 building tree 329 of 1000 building tree 330 of 1000 building tree 331 of 1000 building tree 332 of 1000 building tree 333 of 1000 building tree 334 of 1000 building tree 335 of 1000 building tree 336 of 1000 building tree 337 of 1000 building tree 338 of 1000 building tree 339 of 1000 building tree 340 of 1000 building tree 341 of 1000 building tree 342 of 1000 building tree 343 of 1000 building tree 344 of 1000 building tree 345 of 1000 building tree 346 of 1000 building tree 347 of 1000 building tree 348 of 1000 building tree 349 of 1000 building tree 350 of 1000 building tree 351 of 1000 building tree 352 of 1000
[Parallel(n_jobs=-1)]: Done 341 tasks | elapsed: 9.5min
building tree 353 of 1000 building tree 354 of 1000 building tree 355 of 1000 building tree 356 of 1000 building tree 357 of 1000 building tree 358 of 1000 building tree 359 of 1000 building tree 360 of 1000 building tree 361 of 1000 building tree 362 of 1000 building tree 363 of 1000 building tree 364 of 1000 building tree 365 of 1000 building tree 366 of 1000 building tree 367 of 1000 building tree 368 of 1000 building tree 369 of 1000 building tree 370 of 1000 building tree 371 of 1000 building tree 372 of 1000 building tree 373 of 1000 building tree 374 of 1000 building tree 375 of 1000 building tree 376 of 1000 building tree 377 of 1000 building tree 378 of 1000 building tree 379 of 1000
[Parallel(n_jobs=-1)]: Done 368 tasks | elapsed: 10.2min
building tree 380 of 1000 building tree 381 of 1000 building tree 382 of 1000 building tree 383 of 1000 building tree 384 of 1000 building tree 385 of 1000 building tree 386 of 1000 building tree 387 of 1000 building tree 388 of 1000 building tree 389 of 1000 building tree 390 of 1000 building tree 391 of 1000 building tree 392 of 1000 building tree 393 of 1000 building tree 394 of 1000 building tree 395 of 1000 building tree 396 of 1000 building tree 397 of 1000 building tree 398 of 1000 building tree 399 of 1000 building tree 400 of 1000 building tree 401 of 1000 building tree 402 of 1000 building tree 403 of 1000 building tree 404 of 1000 building tree 405 of 1000 building tree 406 of 1000 building tree 407 of 1000 building tree 408 of 1000
[Parallel(n_jobs=-1)]: Done 397 tasks | elapsed: 11.0min
building tree 409 of 1000 building tree 410 of 1000 building tree 411 of 1000 building tree 412 of 1000 building tree 413 of 1000 building tree 414 of 1000 building tree 415 of 1000 building tree 416 of 1000 building tree 417 of 1000 building tree 418 of 1000 building tree 419 of 1000 building tree 420 of 1000 building tree 421 of 1000 building tree 422 of 1000 building tree 423 of 1000 building tree 424 of 1000 building tree 425 of 1000 building tree 426 of 1000 building tree 427 of 1000 building tree 428 of 1000 building tree 429 of 1000 building tree 430 of 1000 building tree 431 of 1000 building tree 432 of 1000 building tree 433 of 1000 building tree 434 of 1000 building tree 435 of 1000 building tree 436 of 1000 building tree 437 of 1000 building tree 438 of 1000
[Parallel(n_jobs=-1)]: Done 426 tasks | elapsed: 11.8min
building tree 439 of 1000 building tree 440 of 1000 building tree 441 of 1000 building tree 442 of 1000 building tree 443 of 1000 building tree 444 of 1000 building tree 445 of 1000 building tree 446 of 1000 building tree 447 of 1000 building tree 448 of 1000 building tree 449 of 1000 building tree 450 of 1000 building tree 451 of 1000 building tree 452 of 1000 building tree 453 of 1000 building tree 454 of 1000 building tree 455 of 1000 building tree 456 of 1000 building tree 457 of 1000 building tree 458 of 1000 building tree 459 of 1000 building tree 460 of 1000 building tree 461 of 1000 building tree 462 of 1000 building tree 463 of 1000 building tree 464 of 1000 building tree 465 of 1000 building tree 466 of 1000 building tree 467 of 1000 building tree 468 of 1000
[Parallel(n_jobs=-1)]: Done 457 tasks | elapsed: 12.7min
building tree 469 of 1000 building tree 470 of 1000 building tree 471 of 1000 building tree 472 of 1000 building tree 473 of 1000 building tree 474 of 1000 building tree 475 of 1000 building tree 476 of 1000 building tree 477 of 1000 building tree 478 of 1000 building tree 479 of 1000 building tree 480 of 1000 building tree 481 of 1000 building tree 482 of 1000 building tree 483 of 1000 building tree 484 of 1000 building tree 485 of 1000 building tree 486 of 1000 building tree 487 of 1000 building tree 488 of 1000 building tree 489 of 1000 building tree 490 of 1000 building tree 491 of 1000 building tree 492 of 1000 building tree 493 of 1000 building tree 494 of 1000 building tree 495 of 1000 building tree 496 of 1000 building tree 497 of 1000 building tree 498 of 1000 building tree 499 of 1000
[Parallel(n_jobs=-1)]: Done 488 tasks | elapsed: 13.5min
building tree 500 of 1000 building tree 501 of 1000 building tree 502 of 1000 building tree 503 of 1000 building tree 504 of 1000 building tree 505 of 1000 building tree 506 of 1000 building tree 507 of 1000 building tree 508 of 1000 building tree 509 of 1000 building tree 510 of 1000 building tree 511 of 1000 building tree 512 of 1000 building tree 513 of 1000 building tree 514 of 1000 building tree 515 of 1000 building tree 516 of 1000 building tree 517 of 1000 building tree 518 of 1000 building tree 519 of 1000 building tree 520 of 1000 building tree 521 of 1000 building tree 522 of 1000 building tree 523 of 1000 building tree 524 of 1000 building tree 525 of 1000 building tree 526 of 1000 building tree 527 of 1000 building tree 528 of 1000 building tree 529 of 1000 building tree 530 of 1000 building tree 531 of 1000 building tree 532 of 1000 building tree 533 of 1000
[Parallel(n_jobs=-1)]: Done 521 tasks | elapsed: 14.4min
building tree 534 of 1000 building tree 535 of 1000 building tree 536 of 1000 building tree 537 of 1000 building tree 538 of 1000 building tree 539 of 1000 building tree 540 of 1000 building tree 541 of 1000 building tree 542 of 1000 building tree 543 of 1000 building tree 544 of 1000 building tree 545 of 1000 building tree 546 of 1000 building tree 547 of 1000 building tree 548 of 1000 building tree 549 of 1000 building tree 550 of 1000 building tree 551 of 1000 building tree 552 of 1000 building tree 553 of 1000 building tree 554 of 1000 building tree 555 of 1000 building tree 556 of 1000 building tree 557 of 1000 building tree 558 of 1000 building tree 559 of 1000 building tree 560 of 1000 building tree 561 of 1000 building tree 562 of 1000 building tree 563 of 1000 building tree 564 of 1000 building tree 565 of 1000 building tree 566 of 1000 building tree 567 of 1000
[Parallel(n_jobs=-1)]: Done 554 tasks | elapsed: 15.3min
building tree 568 of 1000 building tree 569 of 1000 building tree 570 of 1000 building tree 571 of 1000 building tree 572 of 1000 building tree 573 of 1000 building tree 574 of 1000 building tree 575 of 1000 building tree 576 of 1000 building tree 577 of 1000 building tree 578 of 1000 building tree 579 of 1000 building tree 580 of 1000 building tree 581 of 1000 building tree 582 of 1000 building tree 583 of 1000 building tree 584 of 1000 building tree 585 of 1000 building tree 586 of 1000 building tree 587 of 1000 building tree 588 of 1000 building tree 589 of 1000 building tree 590 of 1000 building tree 591 of 1000 building tree 592 of 1000 building tree 593 of 1000 building tree 594 of 1000 building tree 595 of 1000 building tree 596 of 1000 building tree 597 of 1000 building tree 598 of 1000 building tree 599 of 1000 building tree 600 of 1000
[Parallel(n_jobs=-1)]: Done 589 tasks | elapsed: 16.3min
building tree 601 of 1000 building tree 602 of 1000 building tree 603 of 1000 building tree 604 of 1000 building tree 605 of 1000 building tree 606 of 1000 building tree 607 of 1000 building tree 608 of 1000 building tree 609 of 1000 building tree 610 of 1000 building tree 611 of 1000 building tree 612 of 1000 building tree 613 of 1000 building tree 614 of 1000 building tree 615 of 1000 building tree 616 of 1000 building tree 617 of 1000 building tree 618 of 1000 building tree 619 of 1000 building tree 620 of 1000 building tree 621 of 1000 building tree 622 of 1000 building tree 623 of 1000 building tree 624 of 1000 building tree 625 of 1000 building tree 626 of 1000 building tree 627 of 1000 building tree 628 of 1000 building tree 629 of 1000 building tree 630 of 1000 building tree 631 of 1000 building tree 632 of 1000 building tree 633 of 1000 building tree 634 of 1000 building tree 635 of 1000
[Parallel(n_jobs=-1)]: Done 624 tasks | elapsed: 17.2min
building tree 636 of 1000 building tree 637 of 1000 building tree 638 of 1000 building tree 639 of 1000 building tree 640 of 1000 building tree 641 of 1000 building tree 642 of 1000 building tree 643 of 1000 building tree 644 of 1000 building tree 645 of 1000 building tree 646 of 1000 building tree 647 of 1000 building tree 648 of 1000 building tree 649 of 1000 building tree 650 of 1000 building tree 651 of 1000 building tree 652 of 1000 building tree 653 of 1000 building tree 654 of 1000 building tree 655 of 1000 building tree 656 of 1000 building tree 657 of 1000 building tree 658 of 1000 building tree 659 of 1000 building tree 660 of 1000 building tree 661 of 1000 building tree 662 of 1000 building tree 663 of 1000 building tree 664 of 1000 building tree 665 of 1000 building tree 666 of 1000 building tree 667 of 1000 building tree 668 of 1000 building tree 669 of 1000 building tree 670 of 1000 building tree 671 of 1000 building tree 672 of 1000
[Parallel(n_jobs=-1)]: Done 661 tasks | elapsed: 18.2min
building tree 673 of 1000 building tree 674 of 1000 building tree 675 of 1000 building tree 676 of 1000 building tree 677 of 1000 building tree 678 of 1000 building tree 679 of 1000 building tree 680 of 1000 building tree 681 of 1000 building tree 682 of 1000 building tree 683 of 1000 building tree 684 of 1000 building tree 685 of 1000 building tree 686 of 1000 building tree 687 of 1000 building tree 688 of 1000 building tree 689 of 1000 building tree 690 of 1000 building tree 691 of 1000 building tree 692 of 1000 building tree 693 of 1000 building tree 694 of 1000 building tree 695 of 1000 building tree 696 of 1000 building tree 697 of 1000 building tree 698 of 1000 building tree 699 of 1000 building tree 700 of 1000 building tree 701 of 1000 building tree 702 of 1000 building tree 703 of 1000 building tree 704 of 1000 building tree 705 of 1000 building tree 706 of 1000 building tree 707 of 1000 building tree 708 of 1000 building tree 709 of 1000
[Parallel(n_jobs=-1)]: Done 698 tasks | elapsed: 19.2min
building tree 710 of 1000 building tree 711 of 1000 building tree 712 of 1000 building tree 713 of 1000 building tree 714 of 1000 building tree 715 of 1000 building tree 716 of 1000 building tree 717 of 1000 building tree 718 of 1000 building tree 719 of 1000 building tree 720 of 1000 building tree 721 of 1000 building tree 722 of 1000 building tree 723 of 1000 building tree 724 of 1000 building tree 725 of 1000 building tree 726 of 1000 building tree 727 of 1000 building tree 728 of 1000 building tree 729 of 1000 building tree 730 of 1000 building tree 731 of 1000 building tree 732 of 1000 building tree 733 of 1000 building tree 734 of 1000 building tree 735 of 1000 building tree 736 of 1000 building tree 737 of 1000 building tree 738 of 1000 building tree 739 of 1000 building tree 740 of 1000 building tree 741 of 1000 building tree 742 of 1000 building tree 743 of 1000 building tree 744 of 1000 building tree 745 of 1000 building tree 746 of 1000 building tree 747 of 1000 building tree 748 of 1000
[Parallel(n_jobs=-1)]: Done 737 tasks | elapsed: 20.3min
building tree 749 of 1000 building tree 750 of 1000 building tree 751 of 1000 building tree 752 of 1000 building tree 753 of 1000 building tree 754 of 1000 building tree 755 of 1000 building tree 756 of 1000 building tree 757 of 1000 building tree 758 of 1000 building tree 759 of 1000 building tree 760 of 1000 building tree 761 of 1000 building tree 762 of 1000 building tree 763 of 1000 building tree 764 of 1000 building tree 765 of 1000 building tree 766 of 1000 building tree 767 of 1000 building tree 768 of 1000 building tree 769 of 1000 building tree 770 of 1000 building tree 771 of 1000 building tree 772 of 1000 building tree 773 of 1000 building tree 774 of 1000 building tree 775 of 1000 building tree 776 of 1000 building tree 777 of 1000 building tree 778 of 1000 building tree 779 of 1000 building tree 780 of 1000 building tree 781 of 1000 building tree 782 of 1000 building tree 783 of 1000 building tree 784 of 1000 building tree 785 of 1000 building tree 786 of 1000 building tree 787 of 1000
[Parallel(n_jobs=-1)]: Done 776 tasks | elapsed: 21.3min
building tree 788 of 1000 building tree 789 of 1000 building tree 790 of 1000 building tree 791 of 1000 building tree 792 of 1000 building tree 793 of 1000 building tree 794 of 1000 building tree 795 of 1000 building tree 796 of 1000 building tree 797 of 1000 building tree 798 of 1000 building tree 799 of 1000 building tree 800 of 1000 building tree 801 of 1000 building tree 802 of 1000 building tree 803 of 1000 building tree 804 of 1000 building tree 805 of 1000 building tree 806 of 1000 building tree 807 of 1000 building tree 808 of 1000 building tree 809 of 1000 building tree 810 of 1000 building tree 811 of 1000 building tree 812 of 1000 building tree 813 of 1000 building tree 814 of 1000 building tree 815 of 1000 building tree 816 of 1000 building tree 817 of 1000 building tree 818 of 1000 building tree 819 of 1000 building tree 820 of 1000 building tree 821 of 1000 building tree 822 of 1000 building tree 823 of 1000 building tree 824 of 1000 building tree 825 of 1000 building tree 826 of 1000 building tree 827 of 1000 building tree 828 of 1000
[Parallel(n_jobs=-1)]: Done 817 tasks | elapsed: 22.4min
building tree 829 of 1000 building tree 830 of 1000 building tree 831 of 1000 building tree 832 of 1000 building tree 833 of 1000 building tree 834 of 1000 building tree 835 of 1000 building tree 836 of 1000 building tree 837 of 1000 building tree 838 of 1000 building tree 839 of 1000 building tree 840 of 1000 building tree 841 of 1000 building tree 842 of 1000 building tree 843 of 1000 building tree 844 of 1000 building tree 845 of 1000 building tree 846 of 1000 building tree 847 of 1000 building tree 848 of 1000 building tree 849 of 1000 building tree 850 of 1000 building tree 851 of 1000 building tree 852 of 1000 building tree 853 of 1000 building tree 854 of 1000 building tree 855 of 1000 building tree 856 of 1000 building tree 857 of 1000 building tree 858 of 1000 building tree 859 of 1000 building tree 860 of 1000 building tree 861 of 1000 building tree 862 of 1000 building tree 863 of 1000 building tree 864 of 1000 building tree 865 of 1000 building tree 866 of 1000 building tree 867 of 1000 building tree 868 of 1000 building tree 869 of 1000
[Parallel(n_jobs=-1)]: Done 858 tasks | elapsed: 23.6min
building tree 870 of 1000 building tree 871 of 1000 building tree 872 of 1000 building tree 873 of 1000 building tree 874 of 1000 building tree 875 of 1000 building tree 876 of 1000 building tree 877 of 1000 building tree 878 of 1000 building tree 879 of 1000 building tree 880 of 1000 building tree 881 of 1000 building tree 882 of 1000 building tree 883 of 1000 building tree 884 of 1000 building tree 885 of 1000 building tree 886 of 1000 building tree 887 of 1000 building tree 888 of 1000 building tree 889 of 1000 building tree 890 of 1000 building tree 891 of 1000 building tree 892 of 1000 building tree 893 of 1000 building tree 894 of 1000 building tree 895 of 1000 building tree 896 of 1000 building tree 897 of 1000 building tree 898 of 1000 building tree 899 of 1000 building tree 900 of 1000 building tree 901 of 1000 building tree 902 of 1000 building tree 903 of 1000 building tree 904 of 1000 building tree 905 of 1000 building tree 906 of 1000 building tree 907 of 1000 building tree 908 of 1000 building tree 909 of 1000 building tree 910 of 1000 building tree 911 of 1000 building tree 912 of 1000
[Parallel(n_jobs=-1)]: Done 901 tasks | elapsed: 24.7min
building tree 913 of 1000 building tree 914 of 1000 building tree 915 of 1000 building tree 916 of 1000 building tree 917 of 1000 building tree 918 of 1000 building tree 919 of 1000 building tree 920 of 1000 building tree 921 of 1000 building tree 922 of 1000 building tree 923 of 1000 building tree 924 of 1000 building tree 925 of 1000 building tree 926 of 1000 building tree 927 of 1000 building tree 928 of 1000 building tree 929 of 1000 building tree 930 of 1000 building tree 931 of 1000 building tree 932 of 1000 building tree 933 of 1000 building tree 934 of 1000 building tree 935 of 1000 building tree 936 of 1000 building tree 937 of 1000 building tree 938 of 1000 building tree 939 of 1000 building tree 940 of 1000 building tree 941 of 1000 building tree 942 of 1000 building tree 943 of 1000 building tree 944 of 1000 building tree 945 of 1000 building tree 946 of 1000 building tree 947 of 1000 building tree 948 of 1000 building tree 949 of 1000 building tree 950 of 1000 building tree 951 of 1000 building tree 952 of 1000 building tree 953 of 1000 building tree 954 of 1000 building tree 955 of 1000
[Parallel(n_jobs=-1)]: Done 944 tasks | elapsed: 25.9min
building tree 956 of 1000 building tree 957 of 1000 building tree 958 of 1000 building tree 959 of 1000 building tree 960 of 1000 building tree 961 of 1000 building tree 962 of 1000 building tree 963 of 1000 building tree 964 of 1000 building tree 965 of 1000 building tree 966 of 1000 building tree 967 of 1000 building tree 968 of 1000 building tree 969 of 1000 building tree 970 of 1000 building tree 971 of 1000 building tree 972 of 1000 building tree 973 of 1000 building tree 974 of 1000 building tree 975 of 1000 building tree 976 of 1000 building tree 977 of 1000 building tree 978 of 1000 building tree 979 of 1000 building tree 980 of 1000 building tree 981 of 1000 building tree 982 of 1000 building tree 983 of 1000 building tree 984 of 1000 building tree 985 of 1000 building tree 986 of 1000 building tree 987 of 1000 building tree 988 of 1000 building tree 989 of 1000 building tree 990 of 1000 building tree 991 of 1000 building tree 992 of 1000 building tree 993 of 1000 building tree 994 of 1000 building tree 995 of 1000 building tree 996 of 1000 building tree 997 of 1000 building tree 998 of 1000 building tree 999 of 1000 building tree 1000 of 1000
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 27.3min finished
RandomForestClassifier(bootstrap=False, class_weight=None, criterion='entropy',
max_depth=50, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=2, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=-1, oob_score=False, random_state=None,
verbose=10, warm_start=False)
_jl.dump(best_model, "../models/best_Random_Forest_fake.joblib")
['../models/best_Random_Forest_fake.joblib']
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set.shape
(153993, 787)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s [Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 0.1s [Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 0.4s [Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 0.7s [Parallel(n_jobs=12)]: Done 37 tasks | elapsed: 1.0s [Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 1.2s [Parallel(n_jobs=12)]: Done 61 tasks | elapsed: 1.6s [Parallel(n_jobs=12)]: Done 74 tasks | elapsed: 1.9s [Parallel(n_jobs=12)]: Done 89 tasks | elapsed: 2.2s [Parallel(n_jobs=12)]: Done 104 tasks | elapsed: 2.6s [Parallel(n_jobs=12)]: Done 121 tasks | elapsed: 3.0s [Parallel(n_jobs=12)]: Done 138 tasks | elapsed: 3.4s [Parallel(n_jobs=12)]: Done 157 tasks | elapsed: 3.9s [Parallel(n_jobs=12)]: Done 176 tasks | elapsed: 4.3s [Parallel(n_jobs=12)]: Done 197 tasks | elapsed: 4.9s [Parallel(n_jobs=12)]: Done 218 tasks | elapsed: 5.4s [Parallel(n_jobs=12)]: Done 241 tasks | elapsed: 6.0s [Parallel(n_jobs=12)]: Done 264 tasks | elapsed: 6.5s [Parallel(n_jobs=12)]: Done 289 tasks | elapsed: 7.2s [Parallel(n_jobs=12)]: Done 314 tasks | elapsed: 7.8s [Parallel(n_jobs=12)]: Done 341 tasks | elapsed: 8.5s [Parallel(n_jobs=12)]: Done 368 tasks | elapsed: 9.1s [Parallel(n_jobs=12)]: Done 397 tasks | elapsed: 9.8s [Parallel(n_jobs=12)]: Done 426 tasks | elapsed: 10.5s [Parallel(n_jobs=12)]: Done 457 tasks | elapsed: 11.3s [Parallel(n_jobs=12)]: Done 488 tasks | elapsed: 12.0s [Parallel(n_jobs=12)]: Done 521 tasks | elapsed: 12.9s [Parallel(n_jobs=12)]: Done 554 tasks | elapsed: 13.7s [Parallel(n_jobs=12)]: Done 589 tasks | elapsed: 14.6s [Parallel(n_jobs=12)]: Done 624 tasks | elapsed: 15.4s [Parallel(n_jobs=12)]: Done 661 tasks | elapsed: 16.3s [Parallel(n_jobs=12)]: Done 698 tasks | elapsed: 17.2s [Parallel(n_jobs=12)]: Done 737 tasks | elapsed: 18.2s [Parallel(n_jobs=12)]: Done 776 tasks | elapsed: 19.1s [Parallel(n_jobs=12)]: Done 817 tasks | elapsed: 20.1s [Parallel(n_jobs=12)]: Done 858 tasks | elapsed: 21.2s [Parallel(n_jobs=12)]: Done 901 tasks | elapsed: 22.2s [Parallel(n_jobs=12)]: Done 944 tasks | elapsed: 23.3s
predictions: [0 0 1 ... 0 0 0]
[Parallel(n_jobs=12)]: Done 1000 out of 1000 | elapsed: 24.5s finished
set(predic)
{0, 1}
# evaluate classifier
print("Report for Random Forest classifier:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Random Forest Classifier:", _accuracy_score(test_set['likes'], predic)*100)
Report for Random Forest classifier:
precision recall f1-score support
0 0.68 0.41 0.51 50930
1 0.76 0.90 0.82 103063
accuracy 0.74 153993
macro avg 0.72 0.66 0.67 153993
weighted avg 0.73 0.74 0.72 153993
Accuracy for Random Forest Classifier: 74.14947432675511
# Confusion matrix for Random Forest
print("Confusion Matrix for Random Forest: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for Random Forest:
array([[21058, 29872],
[ 9936, 93127]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("Random Forest ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set[train_set['bin_truth_score']!=-1]
train_set.shape
(438571, 787)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
80
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 306999 samples, validate on 131572 samples Epoch 1/100 306999/306999 [==============================] - 30s 98us/step - loss: 0.6015 - acc: 0.6869 - val_loss: 0.5663 - val_acc: 0.7137 Epoch 2/100 306999/306999 [==============================] - 24s 79us/step - loss: 0.5644 - acc: 0.7154 - val_loss: 0.5518 - val_acc: 0.7288 Epoch 3/100 306999/306999 [==============================] - 24s 80us/step - loss: 0.5541 - acc: 0.7242 - val_loss: 0.5727 - val_acc: 0.7173 Epoch 4/100 306999/306999 [==============================] - 24s 77us/step - loss: 0.5498 - acc: 0.7276 - val_loss: 0.5402 - val_acc: 0.7353 Epoch 5/100 306999/306999 [==============================] - 25s 82us/step - loss: 0.5467 - acc: 0.7291 - val_loss: 0.5481 - val_acc: 0.7297 Epoch 6/100 306999/306999 [==============================] - 25s 80us/step - loss: 0.5438 - acc: 0.7325 - val_loss: 0.5427 - val_acc: 0.7352 Epoch 7/100 306999/306999 [==============================] - 24s 78us/step - loss: 0.5427 - acc: 0.7321 - val_loss: 0.5657 - val_acc: 0.7258 Epoch 8/100 306999/306999 [==============================] - 23s 74us/step - loss: 0.5409 - acc: 0.7333 - val_loss: 0.5452 - val_acc: 0.7307 Epoch 9/100 306999/306999 [==============================] - 25s 82us/step - loss: 0.5399 - acc: 0.7343 - val_loss: 0.5414 - val_acc: 0.7344 Epoch 10/100 306999/306999 [==============================] - 24s 77us/step - loss: 0.5380 - acc: 0.7360 - val_loss: 0.5394 - val_acc: 0.7367 Epoch 11/100 306999/306999 [==============================] - 23s 75us/step - loss: 0.5369 - acc: 0.7362 - val_loss: 0.5342 - val_acc: 0.7394 Epoch 12/100 306999/306999 [==============================] - 22s 73us/step - loss: 0.5357 - acc: 0.7378 - val_loss: 0.5382 - val_acc: 0.7376 Epoch 13/100 306999/306999 [==============================] - 22s 73us/step - loss: 0.5353 - acc: 0.7379 - val_loss: 0.5364 - val_acc: 0.7400 Epoch 14/100 306999/306999 [==============================] - 23s 74us/step - loss: 0.5338 - acc: 0.7385 - val_loss: 0.5361 - val_acc: 0.7408 Epoch 15/100 306999/306999 [==============================] - 22s 72us/step - loss: 0.5335 - acc: 0.7395 - val_loss: 0.5489 - val_acc: 0.7342 Epoch 16/100 306999/306999 [==============================] - 21s 69us/step - loss: 0.5319 - acc: 0.7397 - val_loss: 0.5329 - val_acc: 0.7412 Epoch 17/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5318 - acc: 0.7403 - val_loss: 0.5357 - val_acc: 0.7415 Epoch 18/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5303 - acc: 0.7404 - val_loss: 0.5333 - val_acc: 0.7393 Epoch 19/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5301 - acc: 0.7416 - val_loss: 0.5410 - val_acc: 0.7401 Epoch 20/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5303 - acc: 0.7412 - val_loss: 0.5495 - val_acc: 0.7352 Epoch 21/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5288 - acc: 0.7425 - val_loss: 0.5292 - val_acc: 0.7440 Epoch 22/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5287 - acc: 0.7426 - val_loss: 0.5360 - val_acc: 0.7387 Epoch 23/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5281 - acc: 0.7431 - val_loss: 0.5454 - val_acc: 0.7360 Epoch 24/100 306999/306999 [==============================] - 21s 68us/step - loss: 0.5266 - acc: 0.7439 - val_loss: 0.5505 - val_acc: 0.7377 Epoch 25/100 306999/306999 [==============================] - 21s 69us/step - loss: 0.5270 - acc: 0.7433 - val_loss: 0.5461 - val_acc: 0.7369 Epoch 26/100 306999/306999 [==============================] - 21s 69us/step - loss: 0.5266 - acc: 0.7432 - val_loss: 0.5731 - val_acc: 0.7101 Epoch 27/100 306999/306999 [==============================] - 21s 69us/step - loss: 0.5292 - acc: 0.7417 - val_loss: 0.5589 - val_acc: 0.7399 Epoch 28/100 306999/306999 [==============================] - 22s 70us/step - loss: 0.5257 - acc: 0.7439 - val_loss: 0.5932 - val_acc: 0.7362 Epoch 29/100 306999/306999 [==============================] - 22s 72us/step - loss: 0.5253 - acc: 0.7445 - val_loss: 0.5859 - val_acc: 0.7417 Epoch 30/100 306999/306999 [==============================] - 24s 78us/step - loss: 0.5246 - acc: 0.7448 - val_loss: 0.5809 - val_acc: 0.7397 Epoch 31/100 306999/306999 [==============================] - 22s 72us/step - loss: 0.5274 - acc: 0.7438 - val_loss: 0.5833 - val_acc: 0.7371 Epoch 32/100 306999/306999 [==============================] - 22s 72us/step - loss: 0.5249 - acc: 0.7450 - val_loss: 0.5755 - val_acc: 0.7412 Epoch 33/100 306999/306999 [==============================] - 22s 72us/step - loss: 0.5246 - acc: 0.7448 - val_loss: 0.5392 - val_acc: 0.7373 Epoch 34/100 306999/306999 [==============================] - 22s 73us/step - loss: 0.5240 - acc: 0.7453 - val_loss: 0.5380 - val_acc: 0.7442 Epoch 35/100 306999/306999 [==============================] - 22s 72us/step - loss: 0.5233 - acc: 0.7457 - val_loss: 0.5578 - val_acc: 0.7397 Epoch 36/100 306999/306999 [==============================] - 22s 71us/step - loss: 0.5230 - acc: 0.7452 - val_loss: 0.5813 - val_acc: 0.7399 Epoch 37/100 306999/306999 [==============================] - 22s 70us/step - loss: 0.5224 - acc: 0.7457 - val_loss: 0.5773 - val_acc: 0.7428 Epoch 38/100 306999/306999 [==============================] - 22s 71us/step - loss: 0.5218 - acc: 0.7462 - val_loss: 0.5826 - val_acc: 0.7411 Epoch 39/100 306999/306999 [==============================] - 22s 70us/step - loss: 0.5226 - acc: 0.7463 - val_loss: 0.5797 - val_acc: 0.7433 Epoch 40/100 306999/306999 [==============================] - 22s 70us/step - loss: 0.5215 - acc: 0.7471 - val_loss: 0.5719 - val_acc: 0.7411 Epoch 41/100 306999/306999 [==============================] - 22s 70us/step - loss: 0.5230 - acc: 0.7455 - val_loss: 0.5542 - val_acc: 0.7255 Epoch 42/100 306999/306999 [==============================] - 21s 70us/step - loss: 0.5279 - acc: 0.7414 - val_loss: 0.5689 - val_acc: 0.7371 Epoch 43/100 306999/306999 [==============================] - 22s 71us/step - loss: 0.5207 - acc: 0.7465 - val_loss: 0.5660 - val_acc: 0.7420 Epoch 44/100 306999/306999 [==============================] - 21s 70us/step - loss: 0.5209 - acc: 0.7468 - val_loss: 0.5628 - val_acc: 0.7442 Epoch 45/100 306999/306999 [==============================] - 20s 67us/step - loss: 0.5210 - acc: 0.7471 - val_loss: 0.5751 - val_acc: 0.7384 Epoch 46/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5202 - acc: 0.7466 - val_loss: 0.5986 - val_acc: 0.7347 Epoch 47/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5201 - acc: 0.7471 - val_loss: 0.5670 - val_acc: 0.7426 Epoch 48/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5199 - acc: 0.7479 - val_loss: 0.5761 - val_acc: 0.7436 Epoch 49/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5192 - acc: 0.7479 - val_loss: 0.5831 - val_acc: 0.7419 Epoch 50/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5191 - acc: 0.7474 - val_loss: 0.5882 - val_acc: 0.7405 Epoch 51/100 306999/306999 [==============================] - 20s 67us/step - loss: 0.5198 - acc: 0.7475 - val_loss: 0.5773 - val_acc: 0.7406 Epoch 52/100 306999/306999 [==============================] - 20s 67us/step - loss: 0.5188 - acc: 0.7484 - val_loss: 0.5844 - val_acc: 0.7380 Epoch 53/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5183 - acc: 0.7494 - val_loss: 0.5901 - val_acc: 0.7401 Epoch 54/100 306999/306999 [==============================] - 20s 67us/step - loss: 0.5185 - acc: 0.7483 - val_loss: 0.5839 - val_acc: 0.7434 Epoch 55/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5180 - acc: 0.7492 - val_loss: 0.5861 - val_acc: 0.7441 Epoch 56/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5186 - acc: 0.7481 - val_loss: 0.5783 - val_acc: 0.7400 Epoch 57/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5187 - acc: 0.7484 - val_loss: 0.5845 - val_acc: 0.7431 Epoch 58/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5180 - acc: 0.7490 - val_loss: 0.6011 - val_acc: 0.7428 Epoch 59/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5171 - acc: 0.7492 - val_loss: 0.5731 - val_acc: 0.7454 Epoch 60/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5187 - acc: 0.7484 - val_loss: 0.6184 - val_acc: 0.7342 Epoch 61/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5175 - acc: 0.7487 - val_loss: 0.6044 - val_acc: 0.7372 Epoch 62/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5175 - acc: 0.7491 - val_loss: 0.5877 - val_acc: 0.7409 Epoch 63/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5161 - acc: 0.7503 - val_loss: 0.5970 - val_acc: 0.7435 Epoch 64/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5164 - acc: 0.7499 - val_loss: 0.6022 - val_acc: 0.7432 Epoch 65/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5177 - acc: 0.7486 - val_loss: 0.5970 - val_acc: 0.7408 Epoch 66/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5163 - acc: 0.7497 - val_loss: 0.6019 - val_acc: 0.7426 Epoch 67/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5160 - acc: 0.7499 - val_loss: 0.5921 - val_acc: 0.7435 Epoch 68/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5158 - acc: 0.7501 - val_loss: 0.5989 - val_acc: 0.7414 Epoch 69/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5160 - acc: 0.7502 - val_loss: 0.5861 - val_acc: 0.7439 Epoch 70/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5155 - acc: 0.7502 - val_loss: 0.5820 - val_acc: 0.7433 Epoch 71/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5156 - acc: 0.7501 - val_loss: 0.5895 - val_acc: 0.7413 Epoch 72/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5155 - acc: 0.7500 - val_loss: 0.5861 - val_acc: 0.7421 Epoch 73/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5150 - acc: 0.7513 - val_loss: 0.5808 - val_acc: 0.7405 Epoch 74/100 306999/306999 [==============================] - 21s 68us/step - loss: 0.5152 - acc: 0.7508 - val_loss: 0.5894 - val_acc: 0.7420 Epoch 75/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5145 - acc: 0.7511 - val_loss: 0.6106 - val_acc: 0.7419 Epoch 76/100 306999/306999 [==============================] - 20s 64us/step - loss: 0.5137 - acc: 0.7517 - val_loss: 0.5926 - val_acc: 0.7430 Epoch 77/100 306999/306999 [==============================] - 21s 67us/step - loss: 0.5140 - acc: 0.7510 - val_loss: 0.5935 - val_acc: 0.7418 Epoch 78/100 306999/306999 [==============================] - 22s 73us/step - loss: 0.5136 - acc: 0.7508 - val_loss: 0.5996 - val_acc: 0.7415 Epoch 79/100 306999/306999 [==============================] - 23s 74us/step - loss: 0.5137 - acc: 0.7514 - val_loss: 0.6083 - val_acc: 0.7355 Epoch 80/100 306999/306999 [==============================] - 24s 77us/step - loss: 0.5136 - acc: 0.7511 - val_loss: 0.6018 - val_acc: 0.7445 Epoch 81/100 306999/306999 [==============================] - 24s 77us/step - loss: 0.5134 - acc: 0.7516 - val_loss: 0.5918 - val_acc: 0.7459 Epoch 82/100 306999/306999 [==============================] - 22s 70us/step - loss: 0.5132 - acc: 0.7519 - val_loss: 0.5895 - val_acc: 0.7433 Epoch 83/100 306999/306999 [==============================] - 24s 79us/step - loss: 0.5129 - acc: 0.7516 - val_loss: 0.6002 - val_acc: 0.7399 Epoch 84/100 306999/306999 [==============================] - 24s 79us/step - loss: 0.5133 - acc: 0.7516 - val_loss: 0.6034 - val_acc: 0.7421 Epoch 85/100 306999/306999 [==============================] - 20s 67us/step - loss: 0.5130 - acc: 0.7516 - val_loss: 0.5826 - val_acc: 0.7426 Epoch 86/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5130 - acc: 0.7520 - val_loss: 0.5880 - val_acc: 0.7458 Epoch 87/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5125 - acc: 0.7524 - val_loss: 0.5789 - val_acc: 0.7446 Epoch 88/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5127 - acc: 0.7517 - val_loss: 0.5776 - val_acc: 0.7436 Epoch 89/100 306999/306999 [==============================] - 21s 68us/step - loss: 0.5120 - acc: 0.7520 - val_loss: 0.5866 - val_acc: 0.7444 Epoch 90/100 306999/306999 [==============================] - 21s 68us/step - loss: 0.5134 - acc: 0.7515 - val_loss: 0.5776 - val_acc: 0.7462 Epoch 91/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5118 - acc: 0.7524 - val_loss: 0.5934 - val_acc: 0.7398 Epoch 92/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5120 - acc: 0.7526 - val_loss: 0.5861 - val_acc: 0.7444 Epoch 93/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5121 - acc: 0.7516 - val_loss: 0.5867 - val_acc: 0.7470 Epoch 94/100 306999/306999 [==============================] - 20s 67us/step - loss: 0.5119 - acc: 0.7526 - val_loss: 0.5952 - val_acc: 0.7380 Epoch 95/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5118 - acc: 0.7521 - val_loss: 0.6002 - val_acc: 0.7436 Epoch 96/100 306999/306999 [==============================] - 20s 65us/step - loss: 0.5115 - acc: 0.7528 - val_loss: 0.5829 - val_acc: 0.7447 Epoch 97/100 306999/306999 [==============================] - 20s 66us/step - loss: 0.5111 - acc: 0.7534 - val_loss: 0.5996 - val_acc: 0.7415 Epoch 98/100 306999/306999 [==============================] - 23s 76us/step - loss: 0.5107 - acc: 0.7528 - val_loss: 0.5674 - val_acc: 0.7466 Epoch 99/100 306999/306999 [==============================] - 23s 74us/step - loss: 0.5108 - acc: 0.7530 - val_loss: 0.5815 - val_acc: 0.7454 Epoch 100/100 306999/306999 [==============================] - 23s 74us/step - loss: 0.5106 - acc: 0.7529 - val_loss: 0.5847 - val_acc: 0.7446
<keras.callbacks.History at 0x1ad8c4c5b00>
# Save trained model
classifier.save("../models/trained_deep_neural_network_fake.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
438571/438571 [==============================] - 12s 28us/step [0.5348048495635159, 0.7506811895911467]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set.shape
(153993, 787)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[0.],
[1.],
[1.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.69 0.39 0.50 50930
1 0.75 0.92 0.83 103063
accuracy 0.74 153993
macro avg 0.72 0.65 0.66 153993
weighted avg 0.73 0.74 0.72 153993
Accuracy for Deep Learning approach: 74.06700304559298
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[19674 31256] [ 8679 94384]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
# columns we are going to remove from the dataset
_cols_std = ['cuisine_av_hist', 'coll_score', 'average_stars_review', 'num_reviews_review',
'average_stars_user', 'num_reviews_user',
'av_rat_chinese_cuisine', 'av_rat_japanese_cuisine', 'av_rat_mexican_cuisine', 'av_rat_italian_cuisine',
'av_rat_others_cuisine', 'av_rat_american_cuisine', 'av_rat_korean_cuisine', 'av_rat_mediterranean_cuisine',
'av_rat_thai_cuisine', 'av_rat_asianfusion_cuisine']
_cols_bin = ['cuisine_av_hist_bin', 'bin_truth_score', 'coll_score_bin', 'average_stars_bin_review',
'num_reviews_bin_review', 'average_stars_bin_user', 'num_reviews_bin_user',
'av_rat_chinese_cuisine_bin', 'av_rat_japanese_cuisine_bin', 'av_rat_mexican_cuisine_bin',
'av_rat_italian_cuisine_bin', 'av_rat_others_cuisine_bin', 'av_rat_american_cuisine_bin',
'av_rat_korean_cuisine_bin', 'av_rat_mediterranean_cuisine_bin', 'av_rat_thai_cuisine_bin',
'av_rat_asianfusion_cuisine_bin']
_cols_real = ['cuisine_av_hist_real', 'real_truth_score', 'coll_score_real', 'average_stars_real_review',
'num_reviews_real_review', 'average_stars_real_user', 'num_reviews_real_user',
'av_rat_chinese_cuisine_real', 'av_rat_japanese_cuisine_real', 'av_rat_mexican_cuisine_real',
'av_rat_italian_cuisine_real', 'av_rat_others_cuisine_real', 'av_rat_american_cuisine_real',
'av_rat_korean_cuisine_real', 'av_rat_mediterranean_cuisine_real', 'av_rat_thai_cuisine_real',
'av_rat_asianfusion_cuisine_real']
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set.drop(columns=[*_cols_bin, *_cols_real])
train_set.shape
(558386, 753)
best_model = _jl.load("../models/best_SVM.joblib")
best_model.set_params(verbose=10)
best_model.get_params()
{'C': 0.001,
'class_weight': None,
'dual': True,
'fit_intercept': True,
'intercept_scaling': 1,
'loss': 'squared_hinge',
'max_iter': 50000,
'multi_class': 'ovr',
'penalty': 'l2',
'random_state': 0,
'tol': 0.0001,
'verbose': 10}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[LibLinear]
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=50000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=10)
_jl.dump(best_model, "../models/best_SVM_std.joblib")
['../models/best_SVM_std.joblib']
print("coef:", best_model.coef_)
print("intercept:", best_model.intercept_)
coef: [[-1.45163681e-01 -2.06192678e-01 3.43101354e-01 9.50706829e-02 -7.67267815e-02 1.75285027e-05 -3.59168241e-02 7.69028847e-03 -3.37551424e-05 6.44365981e-01 -8.19044376e-05 9.98650771e-04 2.85675869e-04 -8.07039934e-04 -5.88016148e-04 -6.42377233e-04 1.10320520e-03 -2.13229419e-04 -1.36944603e-04 4.43540753e-04 -4.35994723e-05 6.60548107e-05 -9.40240234e-06 -3.16915159e-04 -1.36081275e-01 7.99684151e-05 -4.15239339e-02 6.73184431e-02 3.00972016e-04 1.54171072e-02 -5.04502256e-04 -2.20501470e-04 4.92195916e-05 1.81076061e-04 3.69069303e-05 -5.94465246e-04 3.11871960e-02 2.33957370e-02 3.60430435e-02 4.00775537e-02 5.32107117e-02 4.93676643e-02 1.37202769e-02 2.57261458e-02 1.96609415e-02 4.11802748e-02 -1.30191311e-01 -9.69832815e-02 -1.33655698e-01 -8.26716794e-02 -1.30493220e-01 -1.47665391e-01 -1.32102583e-01 -1.12734326e-01 -1.15993381e-01 -1.25985560e-01 -1.18534497e-01 -1.16310232e-01 -8.86037761e-02 -8.85625045e-02 -9.81470552e-02 -8.55169544e-02 -9.25029457e-02 -9.31002298e-02 -8.93448873e-02 -8.58822273e-02 6.08734051e-03 2.05795751e-02 1.41080355e-02 2.39124916e-02 2.52071186e-02 -2.27900951e-02 1.23261655e-02 3.62203871e-02 -5.55927439e-02 4.58146366e-02 2.12876520e-02 -8.06569307e-02 -1.36249406e-02 3.25710384e-02 2.78837538e-02 6.69934088e-02 -8.48508124e-03 -7.87466725e-02 -1.35055624e-02 -3.24469542e-02 -3.84007855e-04 -1.56273648e-03 1.46397663e-02 2.61100375e-02 4.14629939e-03 6.17941398e-03 3.62395677e-01 -1.08990669e-02 1.53841378e-02 -9.16119301e-03 1.96047295e-02 1.47209564e-02 4.01644957e-02 -2.06227493e-02 2.23082188e-02 -1.90273869e-01 2.03107121e-02 5.93510277e-02 -2.42890723e-02 -1.20876657e-01 6.00448898e-04 -9.27465261e-02 2.47100759e-02 -2.73764890e-02 -5.80382982e-02 1.49861963e-02 8.21008906e-03 1.22036178e-02 -8.81020521e-03 1.94235319e-02 -6.96733312e-02 1.70162579e-02 1.91164345e-02 7.70633647e-03 3.39672283e-02 -1.30166942e-01 5.28450257e-03 -1.07200769e-01 -2.36470907e-02 2.43130012e-03 -5.27724141e-03 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1.83777613e-02 -1.17397590e-01 -1.14693799e-02 1.21929127e-02 5.62237299e-03 -1.46270968e-02 -9.23993972e-02 1.09020562e-02 1.86699249e-02 -5.25853090e-02 -1.55957411e-02 -2.21546449e-04 -2.97786738e-02 4.86462359e-02 -1.55229633e-02 4.18373542e-02 1.71467714e-02 -5.19918848e-03 3.81667301e-02 3.48579483e-02 -4.92319566e-03 -1.29767823e-02 4.92732989e-02 1.42219888e-02 -5.58502934e-02 -3.24365777e-02 -3.71407945e-02 -3.41474181e-02 -9.26607323e-02 6.07485058e-03 -7.19846901e-03 -3.02702141e-03 -8.06356475e-04 2.36656810e-02 4.61743673e-03 -4.37871909e-02 -1.06015916e-02 7.05669008e-02 1.75549103e-02 2.55293636e-02 -4.88283329e-02 -4.75391122e-02 1.10785048e-02 -2.55689685e-02 6.79462775e-03 4.92229083e-03 1.00597326e-02 -1.71726454e-02 3.02456881e-02 -9.38494730e-03 -5.12942532e-02 -7.08734405e-03 1.65447502e-03 1.13963471e-02 -3.87377211e-02 -2.93049321e-02 -1.51343551e-02 -9.70864886e-04 -5.13886007e-02 8.47746494e-03 1.51853308e-02 1.56862454e-02 1.48857768e-02 1.80193756e-02 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1.03133064e-04 -1.75092711e-03 -3.77780684e-03 1.76045984e-03 -1.04718589e-02 -2.94780620e-02 1.46228167e-02 -2.13851279e-02 1.62646547e-02 -3.62533898e-02 -2.79248386e-02 2.60336894e-02 -2.96256302e-02 3.64294711e-02 4.01782015e-02 -1.15381863e-02 -9.34758284e-03 3.02636863e-03 -4.61973800e-02 -1.43106402e-02 1.26470749e-02 -1.79250218e-02 1.36500911e-02 6.55461631e-02 -8.78659972e-03 2.05077333e-02 -3.16895781e-02 2.11768839e-03 -1.02883030e-03 2.91978692e-03 -7.71149534e-04 8.08574147e-03 3.03699100e-02 -5.42914411e-03 -2.66009296e-02 -7.69779587e-03 -6.34711234e-03 2.06794028e-02 1.60466811e-02 -1.21135486e-02 -2.93338309e-03 -2.53805631e-03 -2.97586051e-02 3.76067664e-04 4.76061734e-02 1.82687395e-02 -1.47586696e-02 -1.72434028e-02 2.25708857e-02 1.09903351e-02 1.79556823e-02 -2.06019069e-03 8.72050733e-03 4.03168509e-03 -1.83472977e-03 -3.23866255e-03 -1.71672810e-04 8.85174742e-03 -2.48307116e-02 2.26861094e-02 1.86370941e-03 -5.40835197e-03 1.53280586e-02 -5.87940610e-02 -1.57940294e-02 2.42943861e-02 -3.52404843e-02 -3.44167363e-02 6.25969029e-03 7.36495219e-02 -6.52754991e-03 2.12075097e-02 1.23134272e-02 2.33817581e-02 3.06627292e-02 7.93489692e-02 9.82910173e-03 7.39245563e-03 1.20416792e-02 5.94771997e-03 1.10721685e-03 5.56443605e-04 -4.19782527e-03 -5.23808454e-03 -1.13613990e-02 2.73392763e-02 -2.39489952e-02 1.87873350e-02 1.13184848e-02 -7.47975279e-03 3.60163882e-02 -2.47352058e-03 -2.73057798e-02 1.19977320e-02 -1.55003681e-03 1.97790657e-02 -1.41076291e-02 -8.15529979e-03 9.45926853e-03 -5.89448011e-03 2.15895408e-02 -2.87217768e-02 -4.17266662e-02 5.28080035e-02 1.69717922e-03 -2.82523414e-02 2.83681481e-02 -3.16756834e-02 1.62397233e-03 -2.87693937e-02 2.39428680e-02 -2.19990863e-02 2.81463295e-03 1.76802144e-02 -1.44572408e-02 -1.12283080e-03 7.26220147e-03 -1.66535437e-03 2.05374763e-02 -3.53192241e-02 -1.88999403e-02 -7.54961289e-03 1.46910010e-02 3.47818228e-02 3.98925887e-03 -3.55625057e-02 2.31097459e-02 -4.34346282e-03 -1.32456198e-02 -5.10248277e-02 4.56924066e-02 -4.85704689e-03 2.26144912e-02 9.84353236e-03 -1.05166838e-02 2.32399374e-03 -3.03348354e-02 2.93245043e-04 -4.65186267e-02 2.42918512e-02 5.39311475e-03 1.20426883e-02 2.30813009e-02 2.62066536e-02 1.25079118e-02 -2.12882894e-03 2.02904988e-02 -1.31141925e-02 2.37712078e-02 -4.68004937e-02 -9.63250959e-04 8.29524536e-04 -1.46294253e-02 -3.42737072e-02 3.02323993e-02 7.60810040e-03 4.83546051e-03 -3.85942774e-02 1.70927471e-02 5.32095535e-03 1.24478457e-02 -7.69370484e-03 1.60844295e-03 1.65410658e-03 5.38865552e-03 -1.93543373e-02 -1.89373202e-03 -3.53189618e-04 -3.95985864e-02 -3.19007089e-02 -9.42702217e-04 -3.43002747e-02 8.29840710e-03 -2.31106105e-03 3.05407410e-02 -3.09533734e-02 6.54975585e-03 0.00000000e+00 -1.35699369e-02 2.26533499e-03 -8.23491702e-03 3.07512672e-03 3.16887105e-03 -2.64596838e-02 -5.75086219e-03 5.26037966e-03 8.10317374e-03 -2.01879255e-02 5.88776060e-03 9.42822804e-03 -1.11042564e-03 -4.18525910e-02 -1.05065751e-02 -5.65779070e-03 -3.04874251e-03 -3.15533008e-02 8.28457531e-03 2.23825082e-02 5.43544811e-03 2.19812500e-02 -3.59679807e-02 -5.93539652e-03 2.86454153e-02 -1.40148849e-02 2.78413217e-02 -4.77900579e-03 5.98548226e-03 3.53573210e-02 -3.65877840e-02 1.96629870e-04 -1.18453721e-02 -1.34378108e-02 -3.01776304e-03 -2.69667338e-02 -1.30932907e-03 -2.08677777e-02 -4.97819246e-03 -2.13434891e-02 -1.19103945e-02 8.72717669e-03 -1.00409073e-03 2.14271770e-03 -2.84418287e-02 5.71533918e-04 4.74604922e-02 1.79895276e-03 -1.30045547e-02 5.43018082e-03 -6.92303704e-03 -5.00501592e-02 -5.57593196e-02 7.84015193e-02 1.42319373e-02 2.09516675e-02 -2.17050750e-03 -1.48057402e-02 -4.37628362e-03 3.64140296e-03 -3.13656581e-02 1.23883263e-02 -1.11975056e-02 0.00000000e+00 3.36492141e-03 -3.85754811e-02 -3.24125403e-03 -3.35970132e-02 -4.26739329e-03 7.26938637e-03 3.34811871e-03 -8.42574043e-03 2.38519007e-03 -5.86873734e-03 -3.20123998e-02 -1.37741663e-02 1.01872997e-02 2.61936145e-03 -7.60006872e-04 -2.10422733e-02 -2.67909105e-02 -1.30986999e-03 -1.64830501e-02 -3.30349047e-02 -4.43917466e-03 -6.25434683e-03 -3.80937199e-04 1.72541077e-03 6.82243224e-03 3.84294714e-04 1.42286898e-02 3.10975022e-03 5.38613989e-03 -1.90145442e-03 4.15426267e-03 -3.16312678e-02 -6.14377429e-02 -4.88267373e-02 3.21877339e-02 4.76579631e-03 7.07853373e-05 9.87813003e-04 2.00657189e-03 -2.68148319e-03 1.64774161e-02 2.23684921e-03 -4.95871863e-03 9.93886413e-03 6.56974139e-03 1.04857756e-02 3.50319806e-03 -4.74414277e-03 6.23655750e-04 6.68540992e-03 3.62368959e-02 -5.04601765e-03 -1.27860877e-02 -4.53196564e-03 1.33891206e-02 9.05215933e-03 1.65056530e-02 4.46993948e-03 -7.02486665e-03 -1.05628404e-02 2.56795794e-02 1.64333464e-03 2.58463523e-02 3.18329206e-02 -6.48480101e-04 1.36059796e-02 -1.58513158e-02 -1.98690304e-03 1.08848003e-02 -4.16122045e-03 8.62606662e-03 -1.40543879e-03 1.02193609e-02 -3.12528080e-02 2.11443671e-03 4.22582619e-05 7.78117494e-03 -2.03078324e-02 -2.35145766e-02 -1.48804847e-02 -3.76437508e-02 1.62879908e-02 2.54061169e-02 7.23435225e-03 2.19549562e-02 1.16170832e-02 2.49230240e-02 -2.29370719e-02 -7.58959791e-03 -3.46521558e-03 2.90983356e-02 -1.09027779e-03 2.17484259e-02 -1.40010872e-02 9.87666984e-03 1.71227705e-03 1.70408978e-02 -1.66326212e-02 -2.45024234e-03 3.42578814e-02 -8.91890662e-04 9.07247035e-03 -1.96779982e-02 -2.74027447e-03 1.24231076e-02 3.84661077e-02 -2.49017355e-02 -2.72390968e-02 9.47283420e-04 1.19708791e-03 1.70745052e-02 4.77623195e-04 7.92084136e-03 -4.39068101e-02 -4.61390943e-03 1.89044803e-02 2.34377255e-02 3.11315514e-02 0.00000000e+00 -6.41677468e-03 2.29271606e-03 3.38308568e-03 -6.46594197e-03]] intercept: [-0.36083029]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set = test_set.drop(columns=[*_cols_bin, *_cols_real])
test_set.shape
(153993, 753)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
predictions: [1 0 1 ... 0 0 0]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Support Vector Machine:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Support Vector Machine:", _accuracy_score(test_set['likes'], predic)*100)
Report for Support Vector Machine:
precision recall f1-score support
0 0.70 0.36 0.48 50930
1 0.75 0.92 0.83 103063
accuracy 0.74 153993
macro avg 0.72 0.64 0.65 153993
weighted avg 0.73 0.74 0.71 153993
Accuracy for Support Vector Machine: 73.79556213594124
# Confusion matrix for SVC
print("Confusion Matrix for SVC: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for SVC:
array([[18342, 32588],
[ 7765, 95298]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("SVM ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set.drop(columns=[*_cols_bin, *_cols_real])
train_set.shape
(558386, 753)
params = _jl.load("../models/best_Random_Forest_2.joblib").get_params()
params['n_jobs'] = -1
params['verbose'] = 10
best_model = _RandomForestClassifier(**params)
best_model.get_params()
{'bootstrap': False,
'class_weight': None,
'criterion': 'entropy',
'max_depth': 50,
'max_features': 'auto',
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_impurity_split': None,
'min_samples_leaf': 2,
'min_samples_split': 10,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 1000,
'n_jobs': -1,
'oob_score': False,
'random_state': None,
'verbose': 10,
'warm_start': False}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.
building tree 1 of 1000building tree 2 of 1000 building tree 3 of 1000 building tree 4 of 1000 building tree 5 of 1000 building tree 6 of 1000 building tree 7 of 1000 building tree 8 of 1000 building tree 9 of 1000 building tree 10 of 1000 building tree 11 of 1000 building tree 12 of 1000 building tree 13 of 1000
[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 20.9s
building tree 14 of 1000 building tree 15 of 1000 building tree 16 of 1000 building tree 17 of 1000 building tree 18 of 1000 building tree 19 of 1000 building tree 20 of 1000
[Parallel(n_jobs=-1)]: Done 8 tasks | elapsed: 23.6s
building tree 21 of 1000 building tree 22 of 1000 building tree 23 of 1000 building tree 24 of 1000 building tree 25 of 1000 building tree 26 of 1000 building tree 27 of 1000 building tree 28 of 1000 building tree 29 of 1000
[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 44.0s
building tree 30 of 1000 building tree 31 of 1000 building tree 32 of 1000 building tree 33 of 1000 building tree 34 of 1000 building tree 35 of 1000 building tree 36 of 1000 building tree 37 of 1000 building tree 38 of 1000
[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 1.1min
building tree 39 of 1000 building tree 40 of 1000 building tree 41 of 1000 building tree 42 of 1000 building tree 43 of 1000 building tree 44 of 1000 building tree 45 of 1000 building tree 46 of 1000 building tree 47 of 1000 building tree 48 of 1000
[Parallel(n_jobs=-1)]: Done 37 tasks | elapsed: 1.4min
building tree 49 of 1000 building tree 50 of 1000 building tree 51 of 1000 building tree 52 of 1000 building tree 53 of 1000 building tree 54 of 1000 building tree 55 of 1000 building tree 56 of 1000 building tree 57 of 1000 building tree 58 of 1000 building tree 59 of 1000
[Parallel(n_jobs=-1)]: Done 48 tasks | elapsed: 1.6min
building tree 60 of 1000 building tree 61 of 1000 building tree 62 of 1000 building tree 63 of 1000 building tree 64 of 1000 building tree 65 of 1000 building tree 66 of 1000 building tree 67 of 1000 building tree 68 of 1000 building tree 69 of 1000 building tree 70 of 1000 building tree 71 of 1000 building tree 72 of 1000
[Parallel(n_jobs=-1)]: Done 61 tasks | elapsed: 2.1min
building tree 73 of 1000 building tree 74 of 1000 building tree 75 of 1000 building tree 76 of 1000 building tree 77 of 1000 building tree 78 of 1000 building tree 79 of 1000 building tree 80 of 1000 building tree 81 of 1000 building tree 82 of 1000 building tree 83 of 1000 building tree 84 of 1000 building tree 85 of 1000
[Parallel(n_jobs=-1)]: Done 74 tasks | elapsed: 2.5min
building tree 86 of 1000 building tree 87 of 1000 building tree 88 of 1000 building tree 89 of 1000 building tree 90 of 1000 building tree 91 of 1000 building tree 92 of 1000 building tree 93 of 1000 building tree 94 of 1000 building tree 95 of 1000 building tree 96 of 1000 building tree 97 of 1000 building tree 98 of 1000 building tree 99 of 1000 building tree 100 of 1000 building tree 101 of 1000
[Parallel(n_jobs=-1)]: Done 89 tasks | elapsed: 3.0min
building tree 102 of 1000 building tree 103 of 1000 building tree 104 of 1000 building tree 105 of 1000 building tree 106 of 1000 building tree 107 of 1000 building tree 108 of 1000 building tree 109 of 1000 building tree 110 of 1000 building tree 111 of 1000 building tree 112 of 1000 building tree 113 of 1000 building tree 114 of 1000 building tree 115 of 1000
[Parallel(n_jobs=-1)]: Done 104 tasks | elapsed: 3.5min
building tree 116 of 1000 building tree 117 of 1000 building tree 118 of 1000 building tree 119 of 1000 building tree 120 of 1000 building tree 121 of 1000 building tree 122 of 1000 building tree 123 of 1000 building tree 124 of 1000 building tree 125 of 1000 building tree 126 of 1000 building tree 127 of 1000 building tree 128 of 1000 building tree 129 of 1000 building tree 130 of 1000 building tree 131 of 1000 building tree 132 of 1000
[Parallel(n_jobs=-1)]: Done 121 tasks | elapsed: 4.0min
building tree 133 of 1000 building tree 134 of 1000 building tree 135 of 1000 building tree 136 of 1000 building tree 137 of 1000 building tree 138 of 1000 building tree 139 of 1000 building tree 140 of 1000 building tree 141 of 1000 building tree 142 of 1000 building tree 143 of 1000 building tree 144 of 1000 building tree 145 of 1000 building tree 146 of 1000 building tree 147 of 1000 building tree 148 of 1000 building tree 149 of 1000 building tree 150 of 1000
[Parallel(n_jobs=-1)]: Done 138 tasks | elapsed: 4.6min
building tree 151 of 1000 building tree 152 of 1000 building tree 153 of 1000 building tree 154 of 1000 building tree 155 of 1000 building tree 156 of 1000 building tree 157 of 1000 building tree 158 of 1000 building tree 159 of 1000 building tree 160 of 1000 building tree 161 of 1000 building tree 162 of 1000 building tree 163 of 1000 building tree 164 of 1000 building tree 165 of 1000 building tree 166 of 1000 building tree 167 of 1000 building tree 168 of 1000
[Parallel(n_jobs=-1)]: Done 157 tasks | elapsed: 5.2min
building tree 169 of 1000 building tree 170 of 1000 building tree 171 of 1000 building tree 172 of 1000 building tree 173 of 1000 building tree 174 of 1000 building tree 175 of 1000 building tree 176 of 1000 building tree 177 of 1000 building tree 178 of 1000 building tree 179 of 1000 building tree 180 of 1000 building tree 181 of 1000 building tree 182 of 1000 building tree 183 of 1000 building tree 184 of 1000 building tree 185 of 1000 building tree 186 of 1000 building tree 187 of 1000 building tree 188 of 1000
[Parallel(n_jobs=-1)]: Done 176 tasks | elapsed: 5.8min
building tree 189 of 1000 building tree 190 of 1000 building tree 191 of 1000 building tree 192 of 1000 building tree 193 of 1000 building tree 194 of 1000 building tree 195 of 1000 building tree 196 of 1000 building tree 197 of 1000 building tree 198 of 1000 building tree 199 of 1000 building tree 200 of 1000 building tree 201 of 1000 building tree 202 of 1000 building tree 203 of 1000 building tree 204 of 1000 building tree 205 of 1000 building tree 206 of 1000 building tree 207 of 1000 building tree 208 of 1000
[Parallel(n_jobs=-1)]: Done 197 tasks | elapsed: 6.5min
building tree 209 of 1000 building tree 210 of 1000 building tree 211 of 1000 building tree 212 of 1000 building tree 213 of 1000 building tree 214 of 1000 building tree 215 of 1000 building tree 216 of 1000 building tree 217 of 1000 building tree 218 of 1000 building tree 219 of 1000 building tree 220 of 1000 building tree 221 of 1000 building tree 222 of 1000 building tree 223 of 1000 building tree 224 of 1000 building tree 225 of 1000 building tree 226 of 1000 building tree 227 of 1000 building tree 228 of 1000 building tree 229 of 1000 building tree 230 of 1000
[Parallel(n_jobs=-1)]: Done 218 tasks | elapsed: 7.3min
building tree 231 of 1000 building tree 232 of 1000 building tree 233 of 1000 building tree 234 of 1000 building tree 235 of 1000 building tree 236 of 1000 building tree 237 of 1000 building tree 238 of 1000 building tree 239 of 1000 building tree 240 of 1000 building tree 241 of 1000 building tree 242 of 1000 building tree 243 of 1000 building tree 244 of 1000 building tree 245 of 1000 building tree 246 of 1000 building tree 247 of 1000 building tree 248 of 1000 building tree 249 of 1000 building tree 250 of 1000 building tree 251 of 1000 building tree 252 of 1000
[Parallel(n_jobs=-1)]: Done 241 tasks | elapsed: 8.0min
building tree 253 of 1000 building tree 254 of 1000 building tree 255 of 1000 building tree 256 of 1000 building tree 257 of 1000 building tree 258 of 1000 building tree 259 of 1000 building tree 260 of 1000 building tree 261 of 1000 building tree 262 of 1000 building tree 263 of 1000 building tree 264 of 1000 building tree 265 of 1000 building tree 266 of 1000 building tree 267 of 1000 building tree 268 of 1000 building tree 269 of 1000 building tree 270 of 1000 building tree 271 of 1000 building tree 272 of 1000 building tree 273 of 1000 building tree 274 of 1000 building tree 275 of 1000
[Parallel(n_jobs=-1)]: Done 264 tasks | elapsed: 8.7min
building tree 276 of 1000 building tree 277 of 1000 building tree 278 of 1000 building tree 279 of 1000 building tree 280 of 1000 building tree 281 of 1000 building tree 282 of 1000 building tree 283 of 1000 building tree 284 of 1000 building tree 285 of 1000 building tree 286 of 1000 building tree 287 of 1000 building tree 288 of 1000 building tree 289 of 1000 building tree 290 of 1000 building tree 291 of 1000 building tree 292 of 1000 building tree 293 of 1000 building tree 294 of 1000 building tree 295 of 1000 building tree 296 of 1000 building tree 297 of 1000 building tree 298 of 1000 building tree 299 of 1000 building tree 300 of 1000
[Parallel(n_jobs=-1)]: Done 289 tasks | elapsed: 9.6min
building tree 301 of 1000 building tree 302 of 1000 building tree 303 of 1000 building tree 304 of 1000 building tree 305 of 1000 building tree 306 of 1000 building tree 307 of 1000 building tree 308 of 1000 building tree 309 of 1000 building tree 310 of 1000 building tree 311 of 1000 building tree 312 of 1000 building tree 313 of 1000 building tree 314 of 1000 building tree 315 of 1000 building tree 316 of 1000 building tree 317 of 1000 building tree 318 of 1000 building tree 319 of 1000 building tree 320 of 1000 building tree 321 of 1000 building tree 322 of 1000 building tree 323 of 1000 building tree 324 of 1000 building tree 325 of 1000
[Parallel(n_jobs=-1)]: Done 314 tasks | elapsed: 10.4min
building tree 326 of 1000 building tree 327 of 1000 building tree 328 of 1000 building tree 329 of 1000 building tree 330 of 1000 building tree 331 of 1000 building tree 332 of 1000 building tree 333 of 1000 building tree 334 of 1000 building tree 335 of 1000 building tree 336 of 1000 building tree 337 of 1000 building tree 338 of 1000 building tree 339 of 1000 building tree 340 of 1000 building tree 341 of 1000 building tree 342 of 1000 building tree 343 of 1000 building tree 344 of 1000 building tree 345 of 1000 building tree 346 of 1000 building tree 347 of 1000 building tree 348 of 1000 building tree 349 of 1000 building tree 350 of 1000 building tree 351 of 1000 building tree 352 of 1000
[Parallel(n_jobs=-1)]: Done 341 tasks | elapsed: 11.2min
building tree 353 of 1000 building tree 354 of 1000 building tree 355 of 1000 building tree 356 of 1000 building tree 357 of 1000 building tree 358 of 1000 building tree 359 of 1000 building tree 360 of 1000 building tree 361 of 1000 building tree 362 of 1000 building tree 363 of 1000 building tree 364 of 1000 building tree 365 of 1000 building tree 366 of 1000 building tree 367 of 1000 building tree 368 of 1000 building tree 369 of 1000 building tree 370 of 1000 building tree 371 of 1000 building tree 372 of 1000 building tree 373 of 1000 building tree 374 of 1000 building tree 375 of 1000 building tree 376 of 1000 building tree 377 of 1000 building tree 378 of 1000 building tree 379 of 1000
[Parallel(n_jobs=-1)]: Done 368 tasks | elapsed: 12.0min
building tree 380 of 1000 building tree 381 of 1000 building tree 382 of 1000 building tree 383 of 1000 building tree 384 of 1000 building tree 385 of 1000 building tree 386 of 1000 building tree 387 of 1000 building tree 388 of 1000 building tree 389 of 1000 building tree 390 of 1000 building tree 391 of 1000 building tree 392 of 1000 building tree 393 of 1000 building tree 394 of 1000 building tree 395 of 1000 building tree 396 of 1000 building tree 397 of 1000 building tree 398 of 1000 building tree 399 of 1000 building tree 400 of 1000 building tree 401 of 1000 building tree 402 of 1000 building tree 403 of 1000 building tree 404 of 1000 building tree 405 of 1000 building tree 406 of 1000 building tree 407 of 1000 building tree 408 of 1000
[Parallel(n_jobs=-1)]: Done 397 tasks | elapsed: 12.9min
building tree 409 of 1000 building tree 410 of 1000 building tree 411 of 1000 building tree 412 of 1000 building tree 413 of 1000 building tree 414 of 1000 building tree 415 of 1000 building tree 416 of 1000 building tree 417 of 1000 building tree 418 of 1000 building tree 419 of 1000 building tree 420 of 1000 building tree 421 of 1000 building tree 422 of 1000 building tree 423 of 1000 building tree 424 of 1000 building tree 425 of 1000 building tree 426 of 1000 building tree 427 of 1000 building tree 428 of 1000 building tree 429 of 1000 building tree 430 of 1000 building tree 431 of 1000 building tree 432 of 1000 building tree 433 of 1000 building tree 434 of 1000 building tree 435 of 1000 building tree 436 of 1000 building tree 437 of 1000
[Parallel(n_jobs=-1)]: Done 426 tasks | elapsed: 13.8min
building tree 438 of 1000 building tree 439 of 1000 building tree 440 of 1000 building tree 441 of 1000 building tree 442 of 1000 building tree 443 of 1000 building tree 444 of 1000 building tree 445 of 1000 building tree 446 of 1000 building tree 447 of 1000 building tree 448 of 1000 building tree 449 of 1000 building tree 450 of 1000 building tree 451 of 1000 building tree 452 of 1000 building tree 453 of 1000 building tree 454 of 1000 building tree 455 of 1000 building tree 456 of 1000 building tree 457 of 1000 building tree 458 of 1000 building tree 459 of 1000 building tree 460 of 1000 building tree 461 of 1000 building tree 462 of 1000 building tree 463 of 1000 building tree 464 of 1000 building tree 465 of 1000 building tree 466 of 1000 building tree 467 of 1000 building tree 468 of 1000
[Parallel(n_jobs=-1)]: Done 457 tasks | elapsed: 14.9min
building tree 469 of 1000 building tree 470 of 1000 building tree 471 of 1000 building tree 472 of 1000 building tree 473 of 1000 building tree 474 of 1000 building tree 475 of 1000 building tree 476 of 1000 building tree 477 of 1000 building tree 478 of 1000 building tree 479 of 1000 building tree 480 of 1000 building tree 481 of 1000 building tree 482 of 1000 building tree 483 of 1000 building tree 484 of 1000 building tree 485 of 1000 building tree 486 of 1000 building tree 487 of 1000 building tree 488 of 1000 building tree 489 of 1000 building tree 490 of 1000 building tree 491 of 1000 building tree 492 of 1000 building tree 493 of 1000 building tree 494 of 1000 building tree 495 of 1000 building tree 496 of 1000 building tree 497 of 1000 building tree 498 of 1000 building tree 499 of 1000
[Parallel(n_jobs=-1)]: Done 488 tasks | elapsed: 15.9min
building tree 500 of 1000 building tree 501 of 1000 building tree 502 of 1000 building tree 503 of 1000 building tree 504 of 1000 building tree 505 of 1000 building tree 506 of 1000 building tree 507 of 1000 building tree 508 of 1000 building tree 509 of 1000 building tree 510 of 1000 building tree 511 of 1000 building tree 512 of 1000 building tree 513 of 1000 building tree 514 of 1000 building tree 515 of 1000 building tree 516 of 1000 building tree 517 of 1000 building tree 518 of 1000 building tree 519 of 1000 building tree 520 of 1000 building tree 521 of 1000 building tree 522 of 1000 building tree 523 of 1000 building tree 524 of 1000 building tree 525 of 1000 building tree 526 of 1000 building tree 527 of 1000 building tree 528 of 1000 building tree 529 of 1000 building tree 530 of 1000 building tree 531 of 1000 building tree 532 of 1000
[Parallel(n_jobs=-1)]: Done 521 tasks | elapsed: 16.9min
building tree 533 of 1000 building tree 534 of 1000 building tree 535 of 1000 building tree 536 of 1000 building tree 537 of 1000 building tree 538 of 1000 building tree 539 of 1000 building tree 540 of 1000 building tree 541 of 1000 building tree 542 of 1000 building tree 543 of 1000 building tree 544 of 1000 building tree 545 of 1000 building tree 546 of 1000 building tree 547 of 1000 building tree 548 of 1000 building tree 549 of 1000 building tree 550 of 1000 building tree 551 of 1000 building tree 552 of 1000 building tree 553 of 1000 building tree 554 of 1000 building tree 555 of 1000 building tree 556 of 1000 building tree 557 of 1000 building tree 558 of 1000 building tree 559 of 1000 building tree 560 of 1000 building tree 561 of 1000 building tree 562 of 1000 building tree 563 of 1000 building tree 564 of 1000 building tree 565 of 1000 building tree 566 of 1000
[Parallel(n_jobs=-1)]: Done 554 tasks | elapsed: 18.0min
building tree 567 of 1000 building tree 568 of 1000 building tree 569 of 1000 building tree 570 of 1000 building tree 571 of 1000 building tree 572 of 1000 building tree 573 of 1000 building tree 574 of 1000 building tree 575 of 1000 building tree 576 of 1000 building tree 577 of 1000 building tree 578 of 1000 building tree 579 of 1000 building tree 580 of 1000 building tree 581 of 1000 building tree 582 of 1000 building tree 583 of 1000 building tree 584 of 1000 building tree 585 of 1000 building tree 586 of 1000 building tree 587 of 1000 building tree 588 of 1000 building tree 589 of 1000 building tree 590 of 1000 building tree 591 of 1000 building tree 592 of 1000 building tree 593 of 1000 building tree 594 of 1000 building tree 595 of 1000 building tree 596 of 1000 building tree 597 of 1000 building tree 598 of 1000 building tree 599 of 1000 building tree 600 of 1000
[Parallel(n_jobs=-1)]: Done 589 tasks | elapsed: 19.2min
building tree 601 of 1000 building tree 602 of 1000 building tree 603 of 1000 building tree 604 of 1000 building tree 605 of 1000 building tree 606 of 1000 building tree 607 of 1000 building tree 608 of 1000 building tree 609 of 1000 building tree 610 of 1000 building tree 611 of 1000 building tree 612 of 1000 building tree 613 of 1000 building tree 614 of 1000 building tree 615 of 1000 building tree 616 of 1000 building tree 617 of 1000 building tree 618 of 1000 building tree 619 of 1000 building tree 620 of 1000 building tree 621 of 1000 building tree 622 of 1000 building tree 623 of 1000 building tree 624 of 1000 building tree 625 of 1000 building tree 626 of 1000 building tree 627 of 1000 building tree 628 of 1000 building tree 629 of 1000 building tree 630 of 1000 building tree 631 of 1000 building tree 632 of 1000 building tree 633 of 1000 building tree 634 of 1000 building tree 635 of 1000 building tree 636 of 1000 building tree 637 of 1000 building tree 638 of 1000
[Parallel(n_jobs=-1)]: Done 624 tasks | elapsed: 20.3min
building tree 639 of 1000 building tree 640 of 1000 building tree 641 of 1000 building tree 642 of 1000 building tree 643 of 1000 building tree 644 of 1000 building tree 645 of 1000 building tree 646 of 1000 building tree 647 of 1000 building tree 648 of 1000 building tree 649 of 1000 building tree 650 of 1000 building tree 651 of 1000 building tree 652 of 1000 building tree 653 of 1000 building tree 654 of 1000 building tree 655 of 1000 building tree 656 of 1000 building tree 657 of 1000 building tree 658 of 1000 building tree 659 of 1000 building tree 660 of 1000 building tree 661 of 1000 building tree 662 of 1000 building tree 663 of 1000 building tree 664 of 1000 building tree 665 of 1000 building tree 666 of 1000 building tree 667 of 1000 building tree 668 of 1000 building tree 669 of 1000 building tree 670 of 1000 building tree 671 of 1000 building tree 672 of 1000
[Parallel(n_jobs=-1)]: Done 661 tasks | elapsed: 21.5min
building tree 673 of 1000 building tree 674 of 1000 building tree 675 of 1000 building tree 676 of 1000 building tree 677 of 1000 building tree 678 of 1000 building tree 679 of 1000 building tree 680 of 1000 building tree 681 of 1000 building tree 682 of 1000 building tree 683 of 1000 building tree 684 of 1000 building tree 685 of 1000 building tree 686 of 1000 building tree 687 of 1000 building tree 688 of 1000 building tree 689 of 1000 building tree 690 of 1000 building tree 691 of 1000 building tree 692 of 1000 building tree 693 of 1000 building tree 694 of 1000 building tree 695 of 1000 building tree 696 of 1000 building tree 697 of 1000 building tree 698 of 1000 building tree 699 of 1000 building tree 700 of 1000 building tree 701 of 1000 building tree 702 of 1000 building tree 703 of 1000 building tree 704 of 1000 building tree 705 of 1000 building tree 706 of 1000 building tree 707 of 1000 building tree 708 of 1000 building tree 709 of 1000 building tree 710 of 1000
[Parallel(n_jobs=-1)]: Done 698 tasks | elapsed: 22.6min
building tree 711 of 1000 building tree 712 of 1000 building tree 713 of 1000 building tree 714 of 1000 building tree 715 of 1000 building tree 716 of 1000 building tree 717 of 1000 building tree 718 of 1000 building tree 719 of 1000 building tree 720 of 1000 building tree 721 of 1000 building tree 722 of 1000 building tree 723 of 1000 building tree 724 of 1000 building tree 725 of 1000 building tree 726 of 1000 building tree 727 of 1000 building tree 728 of 1000 building tree 729 of 1000 building tree 730 of 1000 building tree 731 of 1000 building tree 732 of 1000 building tree 733 of 1000 building tree 734 of 1000 building tree 735 of 1000 building tree 736 of 1000 building tree 737 of 1000 building tree 738 of 1000 building tree 739 of 1000 building tree 740 of 1000 building tree 741 of 1000 building tree 742 of 1000 building tree 743 of 1000 building tree 744 of 1000 building tree 745 of 1000 building tree 746 of 1000 building tree 747 of 1000 building tree 748 of 1000 building tree 749 of 1000
[Parallel(n_jobs=-1)]: Done 737 tasks | elapsed: 23.9min
building tree 750 of 1000 building tree 751 of 1000 building tree 752 of 1000 building tree 753 of 1000 building tree 754 of 1000 building tree 755 of 1000 building tree 756 of 1000 building tree 757 of 1000 building tree 758 of 1000 building tree 759 of 1000 building tree 760 of 1000 building tree 761 of 1000 building tree 762 of 1000 building tree 763 of 1000 building tree 764 of 1000 building tree 765 of 1000 building tree 766 of 1000 building tree 767 of 1000 building tree 768 of 1000 building tree 769 of 1000 building tree 770 of 1000 building tree 771 of 1000 building tree 772 of 1000 building tree 773 of 1000 building tree 774 of 1000 building tree 775 of 1000 building tree 776 of 1000 building tree 777 of 1000 building tree 778 of 1000 building tree 779 of 1000 building tree 780 of 1000 building tree 781 of 1000 building tree 782 of 1000 building tree 783 of 1000 building tree 784 of 1000 building tree 785 of 1000 building tree 786 of 1000 building tree 787 of 1000
[Parallel(n_jobs=-1)]: Done 776 tasks | elapsed: 25.1min
building tree 788 of 1000 building tree 789 of 1000 building tree 790 of 1000 building tree 791 of 1000 building tree 792 of 1000 building tree 793 of 1000 building tree 794 of 1000 building tree 795 of 1000 building tree 796 of 1000 building tree 797 of 1000 building tree 798 of 1000 building tree 799 of 1000 building tree 800 of 1000 building tree 801 of 1000 building tree 802 of 1000 building tree 803 of 1000 building tree 804 of 1000 building tree 805 of 1000 building tree 806 of 1000 building tree 807 of 1000 building tree 808 of 1000 building tree 809 of 1000 building tree 810 of 1000 building tree 811 of 1000 building tree 812 of 1000 building tree 813 of 1000 building tree 814 of 1000 building tree 815 of 1000 building tree 816 of 1000 building tree 817 of 1000 building tree 818 of 1000 building tree 819 of 1000 building tree 820 of 1000 building tree 821 of 1000 building tree 822 of 1000 building tree 823 of 1000 building tree 824 of 1000 building tree 825 of 1000 building tree 826 of 1000 building tree 827 of 1000 building tree 828 of 1000
[Parallel(n_jobs=-1)]: Done 817 tasks | elapsed: 26.4min
building tree 829 of 1000 building tree 830 of 1000 building tree 831 of 1000 building tree 832 of 1000 building tree 833 of 1000 building tree 834 of 1000 building tree 835 of 1000 building tree 836 of 1000 building tree 837 of 1000 building tree 838 of 1000 building tree 839 of 1000 building tree 840 of 1000 building tree 841 of 1000 building tree 842 of 1000 building tree 843 of 1000 building tree 844 of 1000 building tree 845 of 1000 building tree 846 of 1000 building tree 847 of 1000 building tree 848 of 1000 building tree 849 of 1000 building tree 850 of 1000 building tree 851 of 1000 building tree 852 of 1000 building tree 853 of 1000 building tree 854 of 1000 building tree 855 of 1000 building tree 856 of 1000 building tree 857 of 1000 building tree 858 of 1000 building tree 859 of 1000 building tree 860 of 1000 building tree 861 of 1000 building tree 862 of 1000 building tree 863 of 1000 building tree 864 of 1000 building tree 865 of 1000 building tree 866 of 1000 building tree 867 of 1000 building tree 868 of 1000 building tree 869 of 1000 building tree 870 of 1000 building tree 871 of 1000
[Parallel(n_jobs=-1)]: Done 858 tasks | elapsed: 27.7min
building tree 872 of 1000 building tree 873 of 1000 building tree 874 of 1000 building tree 875 of 1000 building tree 876 of 1000 building tree 877 of 1000 building tree 878 of 1000 building tree 879 of 1000 building tree 880 of 1000 building tree 881 of 1000 building tree 882 of 1000 building tree 883 of 1000 building tree 884 of 1000 building tree 885 of 1000 building tree 886 of 1000 building tree 887 of 1000 building tree 888 of 1000 building tree 889 of 1000 building tree 890 of 1000 building tree 891 of 1000 building tree 892 of 1000 building tree 893 of 1000 building tree 894 of 1000 building tree 895 of 1000 building tree 896 of 1000 building tree 897 of 1000 building tree 898 of 1000 building tree 899 of 1000 building tree 900 of 1000 building tree 901 of 1000 building tree 902 of 1000 building tree 903 of 1000 building tree 904 of 1000 building tree 905 of 1000 building tree 906 of 1000 building tree 907 of 1000 building tree 908 of 1000 building tree 909 of 1000 building tree 910 of 1000 building tree 911 of 1000 building tree 912 of 1000
[Parallel(n_jobs=-1)]: Done 901 tasks | elapsed: 29.1min
building tree 913 of 1000 building tree 914 of 1000 building tree 915 of 1000 building tree 916 of 1000 building tree 917 of 1000 building tree 918 of 1000 building tree 919 of 1000 building tree 920 of 1000 building tree 921 of 1000 building tree 922 of 1000 building tree 923 of 1000 building tree 924 of 1000 building tree 925 of 1000 building tree 926 of 1000 building tree 927 of 1000 building tree 928 of 1000 building tree 929 of 1000 building tree 930 of 1000 building tree 931 of 1000 building tree 932 of 1000 building tree 933 of 1000 building tree 934 of 1000 building tree 935 of 1000 building tree 936 of 1000 building tree 937 of 1000 building tree 938 of 1000 building tree 939 of 1000 building tree 940 of 1000 building tree 941 of 1000 building tree 942 of 1000 building tree 943 of 1000 building tree 944 of 1000 building tree 945 of 1000 building tree 946 of 1000 building tree 947 of 1000 building tree 948 of 1000 building tree 949 of 1000 building tree 950 of 1000 building tree 951 of 1000 building tree 952 of 1000 building tree 953 of 1000 building tree 954 of 1000 building tree 955 of 1000
[Parallel(n_jobs=-1)]: Done 944 tasks | elapsed: 30.5min
building tree 956 of 1000 building tree 957 of 1000 building tree 958 of 1000 building tree 959 of 1000 building tree 960 of 1000 building tree 961 of 1000 building tree 962 of 1000 building tree 963 of 1000 building tree 964 of 1000 building tree 965 of 1000 building tree 966 of 1000 building tree 967 of 1000 building tree 968 of 1000 building tree 969 of 1000 building tree 970 of 1000 building tree 971 of 1000 building tree 972 of 1000 building tree 973 of 1000 building tree 974 of 1000 building tree 975 of 1000 building tree 976 of 1000 building tree 977 of 1000 building tree 978 of 1000 building tree 979 of 1000 building tree 980 of 1000 building tree 981 of 1000 building tree 982 of 1000 building tree 983 of 1000 building tree 984 of 1000 building tree 985 of 1000 building tree 986 of 1000 building tree 987 of 1000 building tree 988 of 1000 building tree 989 of 1000 building tree 990 of 1000 building tree 991 of 1000 building tree 992 of 1000 building tree 993 of 1000 building tree 994 of 1000 building tree 995 of 1000 building tree 996 of 1000 building tree 997 of 1000 building tree 998 of 1000 building tree 999 of 1000 building tree 1000 of 1000
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 32.5min finished
RandomForestClassifier(bootstrap=False, class_weight=None, criterion='entropy',
max_depth=50, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=2, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=-1, oob_score=False, random_state=None,
verbose=10, warm_start=False)
_jl.dump(best_model, "../models/best_Random_Forest_std.joblib")
['../models/best_Random_Forest_std.joblib']
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set = test_set.drop(columns=[*_cols_bin, *_cols_real])
test_set.shape
(153993, 753)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.1s [Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 0.2s [Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 0.4s [Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 0.6s [Parallel(n_jobs=12)]: Done 37 tasks | elapsed: 0.9s [Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 1.0s [Parallel(n_jobs=12)]: Done 61 tasks | elapsed: 1.5s [Parallel(n_jobs=12)]: Done 74 tasks | elapsed: 1.7s [Parallel(n_jobs=12)]: Done 89 tasks | elapsed: 2.0s [Parallel(n_jobs=12)]: Done 104 tasks | elapsed: 2.4s [Parallel(n_jobs=12)]: Done 121 tasks | elapsed: 2.8s [Parallel(n_jobs=12)]: Done 138 tasks | elapsed: 3.2s [Parallel(n_jobs=12)]: Done 157 tasks | elapsed: 3.6s [Parallel(n_jobs=12)]: Done 176 tasks | elapsed: 4.1s [Parallel(n_jobs=12)]: Done 197 tasks | elapsed: 4.6s [Parallel(n_jobs=12)]: Done 218 tasks | elapsed: 5.1s [Parallel(n_jobs=12)]: Done 241 tasks | elapsed: 5.7s [Parallel(n_jobs=12)]: Done 264 tasks | elapsed: 6.2s [Parallel(n_jobs=12)]: Done 289 tasks | elapsed: 6.8s [Parallel(n_jobs=12)]: Done 314 tasks | elapsed: 7.4s [Parallel(n_jobs=12)]: Done 341 tasks | elapsed: 8.0s [Parallel(n_jobs=12)]: Done 368 tasks | elapsed: 8.6s [Parallel(n_jobs=12)]: Done 397 tasks | elapsed: 9.2s [Parallel(n_jobs=12)]: Done 426 tasks | elapsed: 10.0s [Parallel(n_jobs=12)]: Done 457 tasks | elapsed: 10.6s [Parallel(n_jobs=12)]: Done 488 tasks | elapsed: 11.4s [Parallel(n_jobs=12)]: Done 521 tasks | elapsed: 12.2s [Parallel(n_jobs=12)]: Done 554 tasks | elapsed: 13.0s [Parallel(n_jobs=12)]: Done 589 tasks | elapsed: 13.8s [Parallel(n_jobs=12)]: Done 624 tasks | elapsed: 14.7s [Parallel(n_jobs=12)]: Done 661 tasks | elapsed: 15.6s [Parallel(n_jobs=12)]: Done 698 tasks | elapsed: 16.5s [Parallel(n_jobs=12)]: Done 737 tasks | elapsed: 17.4s [Parallel(n_jobs=12)]: Done 776 tasks | elapsed: 18.3s [Parallel(n_jobs=12)]: Done 817 tasks | elapsed: 19.3s [Parallel(n_jobs=12)]: Done 858 tasks | elapsed: 20.2s [Parallel(n_jobs=12)]: Done 901 tasks | elapsed: 21.2s [Parallel(n_jobs=12)]: Done 944 tasks | elapsed: 22.2s
predictions: [0 0 1 ... 1 1 0]
[Parallel(n_jobs=12)]: Done 1000 out of 1000 | elapsed: 23.5s finished
set(predic)
{0, 1}
# evaluate classifier
print("Report for Random Forest classifier:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Random Forest Classifier:", _accuracy_score(test_set['likes'], predic)*100)
Report for Random Forest classifier:
precision recall f1-score support
0 0.69 0.40 0.51 50930
1 0.75 0.91 0.83 103063
accuracy 0.74 153993
macro avg 0.72 0.66 0.67 153993
weighted avg 0.73 0.74 0.72 153993
Accuracy for Random Forest Classifier: 74.30337742624666
# Confusion matrix for Random Forest
print("Confusion Matrix for Random Forest: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for Random Forest:
array([[20322, 30608],
[ 8963, 94100]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("Random Forest ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set.drop(columns=[*_cols_bin, *_cols_real])
train_set.shape
(558386, 753)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
107
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 34s 86us/step - loss: 0.5951 - acc: 0.6957 - val_loss: 0.5575 - val_acc: 0.7210 Epoch 2/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5558 - acc: 0.7236 - val_loss: 0.5577 - val_acc: 0.7252 Epoch 3/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5467 - acc: 0.7305 - val_loss: 0.5468 - val_acc: 0.7331 Epoch 4/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5421 - acc: 0.7342 - val_loss: 0.5335 - val_acc: 0.7436 Epoch 5/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5401 - acc: 0.7360 - val_loss: 0.5426 - val_acc: 0.7396 Epoch 6/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5375 - acc: 0.7379 - val_loss: 0.5408 - val_acc: 0.7385 Epoch 7/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5345 - acc: 0.7396 - val_loss: 0.5380 - val_acc: 0.7406 Epoch 8/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5344 - acc: 0.7400 - val_loss: 0.5378 - val_acc: 0.7397 Epoch 9/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5331 - acc: 0.7408 - val_loss: 0.5368 - val_acc: 0.7408 Epoch 10/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5311 - acc: 0.7417 - val_loss: 0.5317 - val_acc: 0.7421 Epoch 11/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5297 - acc: 0.7425 - val_loss: 0.5350 - val_acc: 0.7427 Epoch 12/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5293 - acc: 0.7432 - val_loss: 0.5306 - val_acc: 0.7439 Epoch 13/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5284 - acc: 0.7442 - val_loss: 0.5353 - val_acc: 0.7400 Epoch 14/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5264 - acc: 0.7447 - val_loss: 0.5302 - val_acc: 0.7443 Epoch 15/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5266 - acc: 0.7448 - val_loss: 0.5313 - val_acc: 0.7454 Epoch 16/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5255 - acc: 0.7454 - val_loss: 0.5265 - val_acc: 0.7478 Epoch 17/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5247 - acc: 0.7457 - val_loss: 0.5323 - val_acc: 0.7428 Epoch 18/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5243 - acc: 0.7459 - val_loss: 0.5336 - val_acc: 0.7459 Epoch 19/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5238 - acc: 0.7465 - val_loss: 0.5264 - val_acc: 0.7475 Epoch 20/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5227 - acc: 0.7468 - val_loss: 0.5310 - val_acc: 0.7445 Epoch 21/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5230 - acc: 0.7469 - val_loss: 0.5320 - val_acc: 0.7427 Epoch 22/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5229 - acc: 0.7466 - val_loss: 0.5262 - val_acc: 0.7453 Epoch 23/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5214 - acc: 0.7479 - val_loss: 0.5291 - val_acc: 0.7446 Epoch 24/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5208 - acc: 0.7480 - val_loss: 0.5219 - val_acc: 0.7507 Epoch 25/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5203 - acc: 0.7487 - val_loss: 0.5378 - val_acc: 0.7360 Epoch 26/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5201 - acc: 0.7483 - val_loss: 0.5325 - val_acc: 0.7439 Epoch 27/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5196 - acc: 0.7490 - val_loss: 0.5261 - val_acc: 0.7464 Epoch 28/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5195 - acc: 0.7486 - val_loss: 0.5202 - val_acc: 0.7501 Epoch 29/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5198 - acc: 0.7488 - val_loss: 0.5267 - val_acc: 0.7473 Epoch 30/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5186 - acc: 0.7497 - val_loss: 0.5273 - val_acc: 0.7444 Epoch 31/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5187 - acc: 0.7495 - val_loss: 0.5261 - val_acc: 0.7458 Epoch 32/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5179 - acc: 0.7494 - val_loss: 0.5359 - val_acc: 0.7392 Epoch 33/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5193 - acc: 0.7497 - val_loss: 0.5280 - val_acc: 0.7448 Epoch 34/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5172 - acc: 0.7500 - val_loss: 0.5214 - val_acc: 0.7509 Epoch 35/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5173 - acc: 0.7497 - val_loss: 0.5247 - val_acc: 0.7478 Epoch 36/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5171 - acc: 0.7500 - val_loss: 0.5184 - val_acc: 0.7517 Epoch 37/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5174 - acc: 0.7498 - val_loss: 0.5223 - val_acc: 0.7500 Epoch 38/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5170 - acc: 0.7498 - val_loss: 0.5207 - val_acc: 0.7491 Epoch 39/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5165 - acc: 0.7502 - val_loss: 0.5375 - val_acc: 0.7415 Epoch 40/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5156 - acc: 0.7508 - val_loss: 0.5212 - val_acc: 0.7486 Epoch 41/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5153 - acc: 0.7510 - val_loss: 0.5206 - val_acc: 0.7479 Epoch 42/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5151 - acc: 0.7517 - val_loss: 0.5196 - val_acc: 0.7513 Epoch 43/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5152 - acc: 0.7513 - val_loss: 0.5193 - val_acc: 0.7509 Epoch 44/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5149 - acc: 0.7517 - val_loss: 0.5192 - val_acc: 0.7515 Epoch 45/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5146 - acc: 0.7518 - val_loss: 0.5285 - val_acc: 0.7459 Epoch 46/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5149 - acc: 0.7514 - val_loss: 0.5183 - val_acc: 0.7517 Epoch 47/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5148 - acc: 0.7514 - val_loss: 0.5197 - val_acc: 0.7514 Epoch 48/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5144 - acc: 0.7516 - val_loss: 0.5254 - val_acc: 0.7486 Epoch 49/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5141 - acc: 0.7517 - val_loss: 0.5229 - val_acc: 0.7511 Epoch 50/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5134 - acc: 0.7523 - val_loss: 0.5231 - val_acc: 0.7497 Epoch 51/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5128 - acc: 0.7528 - val_loss: 0.5192 - val_acc: 0.7512 Epoch 52/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5132 - acc: 0.7522 - val_loss: 0.5211 - val_acc: 0.7492 Epoch 53/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5159 - acc: 0.7510 - val_loss: 0.5260 - val_acc: 0.7476 Epoch 54/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5197 - acc: 0.7466 - val_loss: 0.5467 - val_acc: 0.7242 Epoch 55/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5188 - acc: 0.7477 - val_loss: 0.5220 - val_acc: 0.7509 Epoch 56/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5135 - acc: 0.7520 - val_loss: 0.5221 - val_acc: 0.7520 Epoch 57/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5125 - acc: 0.7535 - val_loss: 0.5214 - val_acc: 0.7504 Epoch 58/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5123 - acc: 0.7530 - val_loss: 0.5221 - val_acc: 0.7510 Epoch 59/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5116 - acc: 0.7529 - val_loss: 0.5278 - val_acc: 0.7474 Epoch 60/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5116 - acc: 0.7530 - val_loss: 0.5270 - val_acc: 0.7475 Epoch 61/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5112 - acc: 0.7533 - val_loss: 0.5216 - val_acc: 0.7497 Epoch 62/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5111 - acc: 0.7535 - val_loss: 0.5195 - val_acc: 0.7503 Epoch 63/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5112 - acc: 0.7531 - val_loss: 0.5194 - val_acc: 0.7516 Epoch 64/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5108 - acc: 0.7534 - val_loss: 0.5210 - val_acc: 0.7499 Epoch 65/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5109 - acc: 0.7537 - val_loss: 0.5206 - val_acc: 0.7512 Epoch 66/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5127 - acc: 0.7526 - val_loss: 0.5201 - val_acc: 0.7504 Epoch 67/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5106 - acc: 0.7537 - val_loss: 0.5275 - val_acc: 0.7466 Epoch 68/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5103 - acc: 0.7539 - val_loss: 0.5198 - val_acc: 0.7493 Epoch 69/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5118 - acc: 0.7537 - val_loss: 0.5189 - val_acc: 0.7532 Epoch 70/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5104 - acc: 0.7542 - val_loss: 0.5242 - val_acc: 0.7473 Epoch 71/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5100 - acc: 0.7541 - val_loss: 0.5208 - val_acc: 0.7516 Epoch 72/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5100 - acc: 0.7536 - val_loss: 0.5187 - val_acc: 0.7512 Epoch 73/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5107 - acc: 0.7538 - val_loss: 0.5194 - val_acc: 0.7506 Epoch 74/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5095 - acc: 0.7543 - val_loss: 0.5203 - val_acc: 0.7520 Epoch 75/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5096 - acc: 0.7543 - val_loss: 0.5243 - val_acc: 0.7492 Epoch 76/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5105 - acc: 0.7543 - val_loss: 0.5231 - val_acc: 0.7486 Epoch 77/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5092 - acc: 0.7544 - val_loss: 0.5211 - val_acc: 0.7492 Epoch 78/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5092 - acc: 0.7545 - val_loss: 0.5191 - val_acc: 0.7538 Epoch 79/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5087 - acc: 0.7550 - val_loss: 0.5187 - val_acc: 0.7520 Epoch 80/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5088 - acc: 0.7550 - val_loss: 0.5203 - val_acc: 0.7517 Epoch 81/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5090 - acc: 0.7547 - val_loss: 0.5209 - val_acc: 0.7491 Epoch 82/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5084 - acc: 0.7551 - val_loss: 0.5226 - val_acc: 0.7492 Epoch 83/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5095 - acc: 0.7547 - val_loss: 0.5281 - val_acc: 0.7500 Epoch 84/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5089 - acc: 0.7548 - val_loss: 0.5309 - val_acc: 0.7432 Epoch 85/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5100 - acc: 0.7535 - val_loss: 0.5200 - val_acc: 0.7509 Epoch 86/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5088 - acc: 0.7552 - val_loss: 0.5185 - val_acc: 0.7534 Epoch 87/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5087 - acc: 0.7549 - val_loss: 0.5182 - val_acc: 0.7525 Epoch 88/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5081 - acc: 0.7551 - val_loss: 0.5224 - val_acc: 0.7507 Epoch 89/100 390870/390870 [==============================] - 25s 64us/step - loss: 0.5091 - acc: 0.7548 - val_loss: 0.5283 - val_acc: 0.7477 Epoch 90/100 390870/390870 [==============================] - 25s 65us/step - loss: 0.5080 - acc: 0.7553 - val_loss: 0.5197 - val_acc: 0.7509 Epoch 91/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5077 - acc: 0.7556 - val_loss: 0.5179 - val_acc: 0.7522 Epoch 92/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5075 - acc: 0.7556 - val_loss: 0.5288 - val_acc: 0.7469 Epoch 93/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5087 - acc: 0.7553 - val_loss: 0.5216 - val_acc: 0.7517 Epoch 94/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5096 - acc: 0.7551 - val_loss: 0.5242 - val_acc: 0.7483 Epoch 95/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5096 - acc: 0.7551 - val_loss: 0.5217 - val_acc: 0.7505 Epoch 96/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5102 - acc: 0.7536 - val_loss: 0.5292 - val_acc: 0.7473 Epoch 97/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5079 - acc: 0.7557 - val_loss: 0.5224 - val_acc: 0.7502 Epoch 98/100 390870/390870 [==============================] - 26s 65us/step - loss: 0.5079 - acc: 0.7552 - val_loss: 0.5220 - val_acc: 0.7524 Epoch 99/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5069 - acc: 0.7559 - val_loss: 0.5239 - val_acc: 0.7496 Epoch 100/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5140 - acc: 0.7515 - val_loss: 0.5198 - val_acc: 0.7499
<keras.callbacks.History at 0x1b708c73eb8>
# Save trained model
classifier.save("../models/trained_deep_neural_network_std.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 17s 31us/step [0.5116986021793278, 0.7529630040865652]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set = test_set.drop(columns=[*_cols_bin, *_cols_real])
test_set.shape
(153993, 753)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[0.],
...,
[0.],
[1.],
[0.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.72 0.36 0.48 50930
1 0.75 0.93 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.64 0.65 153993
weighted avg 0.74 0.74 0.71 153993
Accuracy for Deep Learning approach: 74.13713610358913
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[18245 32685] [ 7142 95921]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set.drop(columns=[*_cols_bin, *_cols_std])
train_set.shape
(558386, 754)
best_model = _jl.load("../models/best_SVM.joblib")
best_model.set_params(verbose=10)
best_model.get_params()
{'C': 0.001,
'class_weight': None,
'dual': True,
'fit_intercept': True,
'intercept_scaling': 1,
'loss': 'squared_hinge',
'max_iter': 50000,
'multi_class': 'ovr',
'penalty': 'l2',
'random_state': 0,
'tol': 0.0001,
'verbose': 10}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[LibLinear]
C:\Users\super\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=50000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=10)
_jl.dump(best_model, "../models/best_SVM_real.joblib")
['../models/best_SVM_real.joblib']
print("coef:", best_model.coef_)
print("intercept:", best_model.intercept_)
coef: [[-1.43716297e-01 -2.04535344e-01 3.42412756e-01 -1.70106570e-01 9.42906076e-02 -7.64244164e-02 2.83228461e-05 -3.44367883e-02 7.43154614e-03 -2.10748750e-05 6.42141961e-01 -7.67363295e-05 1.05147224e-03 2.61865749e-04 -7.96263673e-04 -5.67863277e-04 -6.66914030e-04 1.09450014e-03 -1.87557776e-04 -8.91209472e-05 4.19307492e-04 -4.27875482e-05 8.48324927e-05 -8.68450895e-06 -2.98160509e-04 -1.31943690e-01 4.98907004e-05 -4.09473030e-02 2.69595801e-04 1.66887312e-02 -4.39124340e-04 -2.50972181e-04 4.38847068e-05 1.61532968e-04 -9.42543009e-05 6.55236412e-02 -6.76947062e-04 3.02737339e-02 2.37219033e-02 3.53476493e-02 4.01342657e-02 5.32870518e-02 4.85615621e-02 1.44103811e-02 2.56643390e-02 2.03262701e-02 4.17940324e-02 -1.24827229e-01 -9.21161254e-02 -1.28367851e-01 -7.71161849e-02 -1.25495132e-01 -1.42699889e-01 -1.26632389e-01 -1.07560837e-01 -1.11117980e-01 -1.19877143e-01 -1.14195865e-01 -1.11238198e-01 -8.52179200e-02 -8.51377622e-02 -9.34756944e-02 -8.14798291e-02 -8.91023815e-02 -9.11799522e-02 -8.48363095e-02 -8.01925625e-02 6.83784341e-03 1.92254971e-02 1.40058851e-02 2.39106028e-02 2.46003929e-02 -2.27673372e-02 1.29762647e-02 3.11092757e-02 -5.74496098e-02 4.47713432e-02 2.02799171e-02 -7.57741485e-02 -1.44389973e-02 3.17057017e-02 2.86103761e-02 6.51142500e-02 -8.49139531e-03 -7.77972563e-02 -1.30119107e-02 -3.40256582e-02 -1.44796203e-03 -1.38423057e-03 1.31105830e-02 2.60418460e-02 2.33846729e-03 5.29074929e-03 3.48008783e-01 -9.74783977e-03 1.77673098e-02 -8.86489053e-03 1.80711141e-02 1.57005941e-02 4.03685529e-02 -1.99766603e-02 2.17479883e-02 -1.81747456e-01 1.99719398e-02 5.82290558e-02 -2.43132901e-02 -1.15618867e-01 8.72118852e-04 -8.60105516e-02 2.41831332e-02 -2.10875723e-02 -5.56974893e-02 1.37147410e-02 7.75777631e-03 1.20550494e-02 -8.02849787e-03 1.97158718e-02 -6.76034995e-02 1.76035986e-02 1.75296613e-02 7.07208171e-03 3.34970610e-02 -1.20573798e-01 8.31427170e-03 -1.02979836e-01 -2.38829799e-02 1.64392594e-03 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-1.53664685e-02 2.64979387e-02 -3.49239256e-02 -3.38094692e-02 7.92953814e-03 7.47241074e-02 -6.75645710e-03 2.06018288e-02 1.29008264e-02 2.63497889e-02 3.09334173e-02 7.84939100e-02 1.01068985e-02 6.25077315e-03 1.20308144e-02 7.39519295e-03 1.24135458e-03 7.70286332e-04 -5.43830545e-03 -6.38783462e-03 -1.35901699e-02 2.67742151e-02 -2.40821850e-02 1.82810151e-02 1.24718095e-02 -7.62080333e-03 4.03348586e-02 -1.71184613e-03 -2.70636989e-02 1.19119881e-02 -1.64302434e-03 2.00077971e-02 -1.40176110e-02 -8.05835669e-03 9.67086176e-03 -5.18628634e-03 1.93308559e-02 -2.89383369e-02 -4.29723599e-02 5.37350245e-02 3.16453339e-03 -2.64130760e-02 2.77979932e-02 -3.14515490e-02 1.84017732e-03 -2.73363357e-02 2.61905678e-02 -2.08372057e-02 2.83759060e-03 1.82699625e-02 -1.45091797e-02 -7.85236472e-05 7.31865718e-03 -4.21628925e-03 2.07016491e-02 -3.85148502e-02 -1.89099266e-02 -8.58946052e-03 1.67151061e-02 3.49526992e-02 4.18637313e-03 -3.59994317e-02 2.45687128e-02 -3.81881299e-03 -1.06850579e-02 -5.22586771e-02 4.26735946e-02 -2.35013806e-03 2.33494510e-02 9.71528304e-03 -1.07773945e-02 2.52467523e-03 -3.04210778e-02 -1.73296552e-03 -4.49178273e-02 2.49230145e-02 3.92718681e-03 1.22880860e-02 2.34955451e-02 2.65367107e-02 1.44125852e-02 -3.69221456e-04 1.82773207e-02 -1.17576018e-02 2.52757927e-02 -4.46373382e-02 -3.57136625e-04 -2.60679490e-05 -1.57908492e-02 -3.45646799e-02 2.78523427e-02 7.07094907e-03 4.48861252e-03 -3.83012103e-02 1.55599056e-02 3.70129306e-03 1.15512975e-02 -9.01585174e-03 5.39040951e-05 1.66301005e-03 5.38590657e-03 -1.92638044e-02 -2.24117148e-03 -2.78016044e-04 -3.90414502e-02 -3.12567861e-02 -8.96096477e-04 -2.78328104e-02 1.06643622e-02 -2.27281534e-03 3.16658561e-02 -3.20813228e-02 6.39288622e-03 0.00000000e+00 -1.45071139e-02 1.42715026e-03 -7.97438314e-03 3.84988264e-03 2.68315418e-03 -2.65674288e-02 -4.05858718e-03 6.05797036e-03 7.80432532e-03 -1.91687863e-02 6.11712672e-03 9.93745103e-03 -9.50309878e-04 -4.17014796e-02 -1.06940936e-02 -5.59727389e-03 -2.88390609e-03 -3.31251482e-02 7.56702389e-03 2.21977742e-02 6.03846263e-03 2.27310460e-02 -3.58590645e-02 -6.25894466e-03 3.03627124e-02 -1.31549344e-02 2.86701270e-02 -4.38543972e-03 5.96440014e-03 3.65200851e-02 -3.44983708e-02 -3.54270601e-04 -1.17295962e-02 -1.29913844e-02 -4.58019303e-03 -2.70093413e-02 -1.02683551e-03 -2.12796323e-02 -6.28906254e-03 -2.13508993e-02 -1.31002531e-02 8.53354900e-03 -1.01119559e-03 2.53777572e-03 -2.73862488e-02 5.13986485e-04 4.96337590e-02 2.01269116e-03 -1.23967219e-02 5.99338545e-03 -7.39463771e-03 -5.15502493e-02 -5.69401986e-02 8.36397356e-02 1.49669026e-02 1.92547717e-02 -2.40386153e-03 -1.55658920e-02 -4.15788011e-03 3.33850113e-03 -3.07508400e-02 1.30070473e-02 -1.13289582e-02 0.00000000e+00 3.45435182e-03 -3.95516509e-02 -2.22982560e-03 -3.48309267e-02 -7.31128590e-03 5.96541863e-03 3.61227183e-03 -8.78392391e-03 1.37834712e-03 -6.69092052e-03 -3.15195770e-02 -1.38635405e-02 1.00959273e-02 2.76226658e-03 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-2.20166959e-02 -1.72379361e-02 -3.75650065e-02 1.55585526e-02 2.79515661e-02 7.45882964e-03 2.13700843e-02 1.30877206e-02 2.53230154e-02 -2.08090208e-02 -7.53651928e-03 -3.25048945e-03 3.19500741e-02 -1.35874362e-03 2.12078402e-02 -1.43182828e-02 9.31946228e-03 1.82984277e-03 1.18614817e-02 -1.77738308e-02 -6.14562184e-03 3.37351295e-02 -8.87142891e-04 1.04445750e-02 -1.90250165e-02 -2.29921638e-03 1.08701737e-02 3.93571522e-02 -2.64140804e-02 -2.62594850e-02 9.20156567e-04 1.21844794e-03 1.57740349e-02 5.68792667e-04 7.02158502e-03 -4.43659971e-02 -1.70125699e-03 1.79322546e-02 2.69916694e-02 3.34130488e-02 0.00000000e+00 -5.87417755e-03 2.45578056e-03 3.08521576e-03 -6.98591714e-03]] intercept: [-0.34531121]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set = test_set.drop(columns=[*_cols_bin, *_cols_std])
test_set.shape
(153993, 754)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
predictions: [0 0 0 ... 0 0 0]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Support Vector Machine:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Support Vector Machine:", _accuracy_score(test_set['likes'], predic)*100)
Report for Support Vector Machine:
precision recall f1-score support
0 0.55 0.49 0.52 50930
1 0.76 0.81 0.78 103063
accuracy 0.70 153993
macro avg 0.66 0.65 0.65 153993
weighted avg 0.69 0.70 0.69 153993
Accuracy for Support Vector Machine: 70.01292266531595
# Confusion matrix for SVC
print("Confusion Matrix for SVC: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for SVC:
array([[24734, 26196],
[19982, 83081]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("SVM ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set.drop(columns=[*_cols_bin, *_cols_std])
train_set.shape
(558386, 754)
params = _jl.load("../models/best_Random_Forest_2.joblib").get_params()
params['n_jobs'] = -1
params['verbose'] = 10
best_model = _RandomForestClassifier(**params)
best_model.get_params()
{'bootstrap': False,
'class_weight': None,
'criterion': 'entropy',
'max_depth': 50,
'max_features': 'auto',
'max_leaf_nodes': None,
'min_impurity_decrease': 0.0,
'min_impurity_split': None,
'min_samples_leaf': 2,
'min_samples_split': 10,
'min_weight_fraction_leaf': 0.0,
'n_estimators': 1000,
'n_jobs': -1,
'oob_score': False,
'random_state': None,
'verbose': 10,
'warm_start': False}
best_model.fit(train_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.
building tree 1 of 1000building tree 2 of 1000building tree 3 of 1000building tree 4 of 1000 building tree 5 of 1000 building tree 6 of 1000 building tree 7 of 1000 building tree 8 of 1000 building tree 9 of 1000 building tree 10 of 1000 building tree 11 of 1000 building tree 12 of 1000 building tree 13 of 1000
[Parallel(n_jobs=-1)]: Done 1 tasks | elapsed: 21.4s
building tree 14 of 1000 building tree 15 of 1000 building tree 16 of 1000 building tree 17 of 1000 building tree 18 of 1000 building tree 19 of 1000 building tree 20 of 1000 building tree 21 of 1000
[Parallel(n_jobs=-1)]: Done 8 tasks | elapsed: 24.0s
building tree 22 of 1000 building tree 23 of 1000 building tree 24 of 1000 building tree 25 of 1000 building tree 26 of 1000 building tree 27 of 1000 building tree 28 of 1000
[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 46.1s
building tree 29 of 1000 building tree 30 of 1000 building tree 31 of 1000 building tree 32 of 1000 building tree 33 of 1000 building tree 34 of 1000 building tree 35 of 1000 building tree 36 of 1000 building tree 37 of 1000
[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 1.1min
building tree 38 of 1000 building tree 39 of 1000 building tree 40 of 1000 building tree 41 of 1000 building tree 42 of 1000 building tree 43 of 1000 building tree 44 of 1000 building tree 45 of 1000 building tree 46 of 1000 building tree 47 of 1000 building tree 48 of 1000
[Parallel(n_jobs=-1)]: Done 37 tasks | elapsed: 1.5min
building tree 49 of 1000 building tree 50 of 1000 building tree 51 of 1000 building tree 52 of 1000 building tree 53 of 1000 building tree 54 of 1000 building tree 55 of 1000 building tree 56 of 1000 building tree 57 of 1000 building tree 58 of 1000 building tree 59 of 1000 building tree 60 of 1000
[Parallel(n_jobs=-1)]: Done 48 tasks | elapsed: 1.7min
building tree 61 of 1000 building tree 62 of 1000 building tree 63 of 1000 building tree 64 of 1000 building tree 65 of 1000 building tree 66 of 1000 building tree 67 of 1000 building tree 68 of 1000 building tree 69 of 1000 building tree 70 of 1000 building tree 71 of 1000 building tree 72 of 1000
[Parallel(n_jobs=-1)]: Done 61 tasks | elapsed: 2.2min
building tree 73 of 1000 building tree 74 of 1000 building tree 75 of 1000 building tree 76 of 1000 building tree 77 of 1000 building tree 78 of 1000 building tree 79 of 1000 building tree 80 of 1000 building tree 81 of 1000 building tree 82 of 1000 building tree 83 of 1000 building tree 84 of 1000 building tree 85 of 1000
[Parallel(n_jobs=-1)]: Done 74 tasks | elapsed: 2.7min
building tree 86 of 1000 building tree 87 of 1000 building tree 88 of 1000 building tree 89 of 1000 building tree 90 of 1000 building tree 91 of 1000 building tree 92 of 1000 building tree 93 of 1000 building tree 94 of 1000 building tree 95 of 1000 building tree 96 of 1000 building tree 97 of 1000 building tree 98 of 1000 building tree 99 of 1000 building tree 100 of 1000
[Parallel(n_jobs=-1)]: Done 89 tasks | elapsed: 3.2min
building tree 101 of 1000 building tree 102 of 1000 building tree 103 of 1000 building tree 104 of 1000 building tree 105 of 1000 building tree 106 of 1000 building tree 107 of 1000 building tree 108 of 1000 building tree 109 of 1000 building tree 110 of 1000 building tree 111 of 1000 building tree 112 of 1000 building tree 113 of 1000 building tree 114 of 1000 building tree 115 of 1000building tree 116 of 1000
[Parallel(n_jobs=-1)]: Done 104 tasks | elapsed: 3.6min
building tree 117 of 1000 building tree 118 of 1000 building tree 119 of 1000 building tree 120 of 1000 building tree 121 of 1000 building tree 122 of 1000 building tree 123 of 1000 building tree 124 of 1000 building tree 125 of 1000 building tree 126 of 1000 building tree 127 of 1000 building tree 128 of 1000 building tree 129 of 1000 building tree 130 of 1000 building tree 131 of 1000 building tree 132 of 1000
[Parallel(n_jobs=-1)]: Done 121 tasks | elapsed: 4.2min
building tree 133 of 1000 building tree 134 of 1000 building tree 135 of 1000 building tree 136 of 1000 building tree 137 of 1000 building tree 138 of 1000 building tree 139 of 1000 building tree 140 of 1000 building tree 141 of 1000 building tree 142 of 1000 building tree 143 of 1000 building tree 144 of 1000 building tree 145 of 1000 building tree 146 of 1000 building tree 147 of 1000 building tree 148 of 1000 building tree 149 of 1000 building tree 150 of 1000
[Parallel(n_jobs=-1)]: Done 138 tasks | elapsed: 4.7min
building tree 151 of 1000 building tree 152 of 1000 building tree 153 of 1000 building tree 154 of 1000 building tree 155 of 1000 building tree 156 of 1000 building tree 157 of 1000 building tree 158 of 1000 building tree 159 of 1000 building tree 160 of 1000 building tree 161 of 1000 building tree 162 of 1000 building tree 163 of 1000 building tree 164 of 1000 building tree 165 of 1000 building tree 166 of 1000 building tree 167 of 1000 building tree 168 of 1000 building tree 169 of 1000
[Parallel(n_jobs=-1)]: Done 157 tasks | elapsed: 5.4min
building tree 170 of 1000 building tree 171 of 1000 building tree 172 of 1000 building tree 173 of 1000 building tree 174 of 1000 building tree 175 of 1000 building tree 176 of 1000 building tree 177 of 1000 building tree 178 of 1000 building tree 179 of 1000 building tree 180 of 1000 building tree 181 of 1000 building tree 182 of 1000 building tree 183 of 1000 building tree 184 of 1000 building tree 185 of 1000 building tree 186 of 1000 building tree 187 of 1000 building tree 188 of 1000 building tree 189 of 1000
[Parallel(n_jobs=-1)]: Done 176 tasks | elapsed: 6.0min
building tree 190 of 1000 building tree 191 of 1000 building tree 192 of 1000 building tree 193 of 1000 building tree 194 of 1000 building tree 195 of 1000 building tree 196 of 1000 building tree 197 of 1000 building tree 198 of 1000 building tree 199 of 1000 building tree 200 of 1000 building tree 201 of 1000 building tree 202 of 1000 building tree 203 of 1000 building tree 204 of 1000 building tree 205 of 1000 building tree 206 of 1000 building tree 207 of 1000 building tree 208 of 1000 building tree 209 of 1000
[Parallel(n_jobs=-1)]: Done 197 tasks | elapsed: 6.7min
building tree 210 of 1000 building tree 211 of 1000 building tree 212 of 1000 building tree 213 of 1000 building tree 214 of 1000 building tree 215 of 1000 building tree 216 of 1000 building tree 217 of 1000 building tree 218 of 1000 building tree 219 of 1000 building tree 220 of 1000 building tree 221 of 1000 building tree 222 of 1000 building tree 223 of 1000 building tree 224 of 1000 building tree 225 of 1000 building tree 226 of 1000 building tree 227 of 1000 building tree 228 of 1000 building tree 229 of 1000
[Parallel(n_jobs=-1)]: Done 218 tasks | elapsed: 7.6min
building tree 230 of 1000 building tree 231 of 1000 building tree 232 of 1000 building tree 233 of 1000 building tree 234 of 1000 building tree 235 of 1000 building tree 236 of 1000 building tree 237 of 1000 building tree 238 of 1000 building tree 239 of 1000 building tree 240 of 1000 building tree 241 of 1000 building tree 242 of 1000 building tree 243 of 1000 building tree 244 of 1000 building tree 245 of 1000 building tree 246 of 1000 building tree 247 of 1000 building tree 248 of 1000 building tree 249 of 1000 building tree 250 of 1000 building tree 251 of 1000 building tree 252 of 1000
[Parallel(n_jobs=-1)]: Done 241 tasks | elapsed: 8.3min
building tree 253 of 1000 building tree 254 of 1000 building tree 255 of 1000 building tree 256 of 1000 building tree 257 of 1000 building tree 258 of 1000 building tree 259 of 1000 building tree 260 of 1000 building tree 261 of 1000 building tree 262 of 1000 building tree 263 of 1000 building tree 264 of 1000 building tree 265 of 1000 building tree 266 of 1000 building tree 267 of 1000 building tree 268 of 1000 building tree 269 of 1000 building tree 270 of 1000 building tree 271 of 1000 building tree 272 of 1000 building tree 273 of 1000 building tree 274 of 1000 building tree 275 of 1000
[Parallel(n_jobs=-1)]: Done 264 tasks | elapsed: 9.1min
building tree 276 of 1000 building tree 277 of 1000 building tree 278 of 1000 building tree 279 of 1000 building tree 280 of 1000 building tree 281 of 1000 building tree 282 of 1000 building tree 283 of 1000 building tree 284 of 1000 building tree 285 of 1000 building tree 286 of 1000 building tree 287 of 1000 building tree 288 of 1000 building tree 289 of 1000 building tree 290 of 1000 building tree 291 of 1000 building tree 292 of 1000 building tree 293 of 1000 building tree 294 of 1000 building tree 295 of 1000 building tree 296 of 1000 building tree 297 of 1000 building tree 298 of 1000 building tree 299 of 1000 building tree 300 of 1000 building tree 301 of 1000
[Parallel(n_jobs=-1)]: Done 289 tasks | elapsed: 9.9min
building tree 302 of 1000 building tree 303 of 1000 building tree 304 of 1000 building tree 305 of 1000 building tree 306 of 1000 building tree 307 of 1000 building tree 308 of 1000 building tree 309 of 1000 building tree 310 of 1000 building tree 311 of 1000 building tree 312 of 1000 building tree 313 of 1000 building tree 314 of 1000 building tree 315 of 1000 building tree 316 of 1000 building tree 317 of 1000 building tree 318 of 1000 building tree 319 of 1000 building tree 320 of 1000 building tree 321 of 1000 building tree 322 of 1000 building tree 323 of 1000 building tree 324 of 1000 building tree 325 of 1000
[Parallel(n_jobs=-1)]: Done 314 tasks | elapsed: 10.8min
building tree 326 of 1000 building tree 327 of 1000 building tree 328 of 1000 building tree 329 of 1000 building tree 330 of 1000 building tree 331 of 1000 building tree 332 of 1000 building tree 333 of 1000 building tree 334 of 1000 building tree 335 of 1000 building tree 336 of 1000 building tree 337 of 1000 building tree 338 of 1000 building tree 339 of 1000 building tree 340 of 1000 building tree 341 of 1000 building tree 342 of 1000 building tree 343 of 1000 building tree 344 of 1000 building tree 345 of 1000 building tree 346 of 1000 building tree 347 of 1000 building tree 348 of 1000 building tree 349 of 1000 building tree 350 of 1000 building tree 351 of 1000 building tree 352 of 1000
[Parallel(n_jobs=-1)]: Done 341 tasks | elapsed: 11.6min
building tree 353 of 1000 building tree 354 of 1000 building tree 355 of 1000 building tree 356 of 1000 building tree 357 of 1000 building tree 358 of 1000 building tree 359 of 1000 building tree 360 of 1000 building tree 361 of 1000 building tree 362 of 1000 building tree 363 of 1000 building tree 364 of 1000 building tree 365 of 1000 building tree 366 of 1000 building tree 367 of 1000 building tree 368 of 1000 building tree 369 of 1000 building tree 370 of 1000 building tree 371 of 1000 building tree 372 of 1000 building tree 373 of 1000 building tree 374 of 1000 building tree 375 of 1000 building tree 376 of 1000 building tree 377 of 1000 building tree 378 of 1000 building tree 379 of 1000 building tree 380 of 1000
[Parallel(n_jobs=-1)]: Done 368 tasks | elapsed: 12.5min
building tree 381 of 1000 building tree 382 of 1000 building tree 383 of 1000 building tree 384 of 1000 building tree 385 of 1000 building tree 386 of 1000 building tree 387 of 1000 building tree 388 of 1000 building tree 389 of 1000 building tree 390 of 1000 building tree 391 of 1000 building tree 392 of 1000 building tree 393 of 1000 building tree 394 of 1000 building tree 395 of 1000 building tree 396 of 1000 building tree 397 of 1000 building tree 398 of 1000 building tree 399 of 1000 building tree 400 of 1000 building tree 401 of 1000 building tree 402 of 1000 building tree 403 of 1000 building tree 404 of 1000 building tree 405 of 1000 building tree 406 of 1000 building tree 407 of 1000 building tree 408 of 1000
[Parallel(n_jobs=-1)]: Done 397 tasks | elapsed: 13.5min
building tree 409 of 1000 building tree 410 of 1000 building tree 411 of 1000 building tree 412 of 1000 building tree 413 of 1000 building tree 414 of 1000 building tree 415 of 1000 building tree 416 of 1000 building tree 417 of 1000 building tree 418 of 1000 building tree 419 of 1000 building tree 420 of 1000 building tree 421 of 1000 building tree 422 of 1000 building tree 423 of 1000 building tree 424 of 1000 building tree 425 of 1000 building tree 426 of 1000 building tree 427 of 1000 building tree 428 of 1000 building tree 429 of 1000 building tree 430 of 1000 building tree 431 of 1000 building tree 432 of 1000 building tree 433 of 1000 building tree 434 of 1000 building tree 435 of 1000 building tree 436 of 1000 building tree 437 of 1000 building tree 438 of 1000 building tree 439 of 1000
[Parallel(n_jobs=-1)]: Done 426 tasks | elapsed: 14.4min
building tree 440 of 1000 building tree 441 of 1000 building tree 442 of 1000 building tree 443 of 1000 building tree 444 of 1000 building tree 445 of 1000 building tree 446 of 1000 building tree 447 of 1000 building tree 448 of 1000 building tree 449 of 1000 building tree 450 of 1000 building tree 451 of 1000 building tree 452 of 1000 building tree 453 of 1000 building tree 454 of 1000 building tree 455 of 1000 building tree 456 of 1000 building tree 457 of 1000 building tree 458 of 1000 building tree 459 of 1000 building tree 460 of 1000 building tree 461 of 1000 building tree 462 of 1000 building tree 463 of 1000 building tree 464 of 1000 building tree 465 of 1000 building tree 466 of 1000 building tree 467 of 1000 building tree 468 of 1000
[Parallel(n_jobs=-1)]: Done 457 tasks | elapsed: 15.5min
building tree 469 of 1000 building tree 470 of 1000 building tree 471 of 1000 building tree 472 of 1000 building tree 473 of 1000 building tree 474 of 1000 building tree 475 of 1000 building tree 476 of 1000 building tree 477 of 1000 building tree 478 of 1000 building tree 479 of 1000 building tree 480 of 1000 building tree 481 of 1000 building tree 482 of 1000 building tree 483 of 1000 building tree 484 of 1000 building tree 485 of 1000 building tree 486 of 1000 building tree 487 of 1000 building tree 488 of 1000 building tree 489 of 1000 building tree 490 of 1000 building tree 491 of 1000 building tree 492 of 1000 building tree 493 of 1000 building tree 494 of 1000 building tree 495 of 1000 building tree 496 of 1000 building tree 497 of 1000 building tree 498 of 1000 building tree 499 of 1000 building tree 500 of 1000
[Parallel(n_jobs=-1)]: Done 488 tasks | elapsed: 16.5min
building tree 501 of 1000 building tree 502 of 1000 building tree 503 of 1000 building tree 504 of 1000 building tree 505 of 1000 building tree 506 of 1000 building tree 507 of 1000 building tree 508 of 1000 building tree 509 of 1000 building tree 510 of 1000 building tree 511 of 1000 building tree 512 of 1000 building tree 513 of 1000 building tree 514 of 1000 building tree 515 of 1000 building tree 516 of 1000 building tree 517 of 1000 building tree 518 of 1000 building tree 519 of 1000 building tree 520 of 1000 building tree 521 of 1000 building tree 522 of 1000 building tree 523 of 1000 building tree 524 of 1000 building tree 525 of 1000 building tree 526 of 1000 building tree 527 of 1000 building tree 528 of 1000 building tree 529 of 1000 building tree 530 of 1000 building tree 531 of 1000 building tree 532 of 1000
[Parallel(n_jobs=-1)]: Done 521 tasks | elapsed: 17.7min
building tree 533 of 1000 building tree 534 of 1000 building tree 535 of 1000 building tree 536 of 1000 building tree 537 of 1000 building tree 538 of 1000 building tree 539 of 1000 building tree 540 of 1000 building tree 541 of 1000 building tree 542 of 1000 building tree 543 of 1000 building tree 544 of 1000 building tree 545 of 1000 building tree 546 of 1000 building tree 547 of 1000 building tree 548 of 1000 building tree 549 of 1000 building tree 550 of 1000 building tree 551 of 1000 building tree 552 of 1000 building tree 553 of 1000 building tree 554 of 1000 building tree 555 of 1000 building tree 556 of 1000 building tree 557 of 1000 building tree 558 of 1000 building tree 559 of 1000 building tree 560 of 1000 building tree 561 of 1000 building tree 562 of 1000 building tree 563 of 1000 building tree 564 of 1000 building tree 565 of 1000
[Parallel(n_jobs=-1)]: Done 554 tasks | elapsed: 18.7min
building tree 566 of 1000 building tree 567 of 1000 building tree 568 of 1000 building tree 569 of 1000 building tree 570 of 1000 building tree 571 of 1000 building tree 572 of 1000 building tree 573 of 1000 building tree 574 of 1000 building tree 575 of 1000 building tree 576 of 1000 building tree 577 of 1000 building tree 578 of 1000 building tree 579 of 1000 building tree 580 of 1000 building tree 581 of 1000 building tree 582 of 1000 building tree 583 of 1000 building tree 584 of 1000 building tree 585 of 1000 building tree 586 of 1000 building tree 587 of 1000 building tree 588 of 1000 building tree 589 of 1000 building tree 590 of 1000 building tree 591 of 1000 building tree 592 of 1000 building tree 593 of 1000 building tree 594 of 1000 building tree 595 of 1000 building tree 596 of 1000 building tree 597 of 1000 building tree 598 of 1000 building tree 599 of 1000 building tree 600 of 1000 building tree 601 of 1000
[Parallel(n_jobs=-1)]: Done 589 tasks | elapsed: 19.9min
building tree 602 of 1000 building tree 603 of 1000 building tree 604 of 1000 building tree 605 of 1000 building tree 606 of 1000 building tree 607 of 1000 building tree 608 of 1000 building tree 609 of 1000 building tree 610 of 1000 building tree 611 of 1000 building tree 612 of 1000 building tree 613 of 1000 building tree 614 of 1000 building tree 615 of 1000 building tree 616 of 1000 building tree 617 of 1000 building tree 618 of 1000 building tree 619 of 1000 building tree 620 of 1000 building tree 621 of 1000 building tree 622 of 1000 building tree 623 of 1000 building tree 624 of 1000 building tree 625 of 1000 building tree 626 of 1000 building tree 627 of 1000 building tree 628 of 1000 building tree 629 of 1000 building tree 630 of 1000 building tree 631 of 1000 building tree 632 of 1000 building tree 633 of 1000 building tree 634 of 1000 building tree 635 of 1000
[Parallel(n_jobs=-1)]: Done 624 tasks | elapsed: 21.0min
building tree 636 of 1000 building tree 637 of 1000 building tree 638 of 1000 building tree 639 of 1000 building tree 640 of 1000 building tree 641 of 1000 building tree 642 of 1000 building tree 643 of 1000 building tree 644 of 1000 building tree 645 of 1000 building tree 646 of 1000 building tree 647 of 1000 building tree 648 of 1000 building tree 649 of 1000 building tree 650 of 1000 building tree 651 of 1000 building tree 652 of 1000 building tree 653 of 1000 building tree 654 of 1000 building tree 655 of 1000 building tree 656 of 1000 building tree 657 of 1000 building tree 658 of 1000 building tree 659 of 1000 building tree 660 of 1000 building tree 661 of 1000 building tree 662 of 1000 building tree 663 of 1000 building tree 664 of 1000 building tree 665 of 1000 building tree 666 of 1000 building tree 667 of 1000 building tree 668 of 1000 building tree 669 of 1000 building tree 670 of 1000 building tree 671 of 1000 building tree 672 of 1000 building tree 673 of 1000
[Parallel(n_jobs=-1)]: Done 661 tasks | elapsed: 22.1min
building tree 674 of 1000 building tree 675 of 1000 building tree 676 of 1000 building tree 677 of 1000 building tree 678 of 1000 building tree 679 of 1000 building tree 680 of 1000 building tree 681 of 1000 building tree 682 of 1000 building tree 683 of 1000 building tree 684 of 1000 building tree 685 of 1000 building tree 686 of 1000 building tree 687 of 1000 building tree 688 of 1000 building tree 689 of 1000 building tree 690 of 1000 building tree 691 of 1000 building tree 692 of 1000 building tree 693 of 1000 building tree 694 of 1000 building tree 695 of 1000 building tree 696 of 1000 building tree 697 of 1000 building tree 698 of 1000 building tree 699 of 1000 building tree 700 of 1000 building tree 701 of 1000 building tree 702 of 1000 building tree 703 of 1000 building tree 704 of 1000 building tree 705 of 1000 building tree 706 of 1000 building tree 707 of 1000 building tree 708 of 1000 building tree 709 of 1000
[Parallel(n_jobs=-1)]: Done 698 tasks | elapsed: 23.4min
building tree 710 of 1000 building tree 711 of 1000 building tree 712 of 1000 building tree 713 of 1000 building tree 714 of 1000 building tree 715 of 1000 building tree 716 of 1000 building tree 717 of 1000 building tree 718 of 1000 building tree 719 of 1000 building tree 720 of 1000 building tree 721 of 1000 building tree 722 of 1000 building tree 723 of 1000 building tree 724 of 1000 building tree 725 of 1000 building tree 726 of 1000 building tree 727 of 1000 building tree 728 of 1000 building tree 729 of 1000 building tree 730 of 1000 building tree 731 of 1000 building tree 732 of 1000 building tree 733 of 1000 building tree 734 of 1000 building tree 735 of 1000 building tree 736 of 1000 building tree 737 of 1000 building tree 738 of 1000 building tree 739 of 1000 building tree 740 of 1000 building tree 741 of 1000 building tree 742 of 1000 building tree 743 of 1000 building tree 744 of 1000 building tree 745 of 1000 building tree 746 of 1000 building tree 747 of 1000 building tree 748 of 1000
[Parallel(n_jobs=-1)]: Done 737 tasks | elapsed: 24.7min
building tree 749 of 1000 building tree 750 of 1000 building tree 751 of 1000 building tree 752 of 1000 building tree 753 of 1000 building tree 754 of 1000 building tree 755 of 1000 building tree 756 of 1000 building tree 757 of 1000 building tree 758 of 1000 building tree 759 of 1000 building tree 760 of 1000 building tree 761 of 1000 building tree 762 of 1000 building tree 763 of 1000 building tree 764 of 1000 building tree 765 of 1000 building tree 766 of 1000 building tree 767 of 1000 building tree 768 of 1000 building tree 769 of 1000 building tree 770 of 1000 building tree 771 of 1000 building tree 772 of 1000 building tree 773 of 1000 building tree 774 of 1000 building tree 775 of 1000 building tree 776 of 1000 building tree 777 of 1000 building tree 778 of 1000 building tree 779 of 1000 building tree 780 of 1000 building tree 781 of 1000 building tree 782 of 1000 building tree 783 of 1000 building tree 784 of 1000 building tree 785 of 1000 building tree 786 of 1000 building tree 787 of 1000 building tree 788 of 1000
[Parallel(n_jobs=-1)]: Done 776 tasks | elapsed: 26.0min
building tree 789 of 1000 building tree 790 of 1000 building tree 791 of 1000 building tree 792 of 1000 building tree 793 of 1000 building tree 794 of 1000 building tree 795 of 1000 building tree 796 of 1000 building tree 797 of 1000 building tree 798 of 1000 building tree 799 of 1000 building tree 800 of 1000 building tree 801 of 1000 building tree 802 of 1000 building tree 803 of 1000 building tree 804 of 1000 building tree 805 of 1000 building tree 806 of 1000 building tree 807 of 1000 building tree 808 of 1000 building tree 809 of 1000 building tree 810 of 1000 building tree 811 of 1000 building tree 812 of 1000 building tree 813 of 1000 building tree 814 of 1000 building tree 815 of 1000 building tree 816 of 1000 building tree 817 of 1000 building tree 818 of 1000 building tree 819 of 1000 building tree 820 of 1000 building tree 821 of 1000 building tree 822 of 1000 building tree 823 of 1000 building tree 824 of 1000 building tree 825 of 1000 building tree 826 of 1000 building tree 827 of 1000 building tree 828 of 1000
[Parallel(n_jobs=-1)]: Done 817 tasks | elapsed: 27.4min
building tree 829 of 1000 building tree 830 of 1000 building tree 831 of 1000 building tree 832 of 1000 building tree 833 of 1000 building tree 834 of 1000 building tree 835 of 1000 building tree 836 of 1000 building tree 837 of 1000 building tree 838 of 1000 building tree 839 of 1000 building tree 840 of 1000 building tree 841 of 1000 building tree 842 of 1000 building tree 843 of 1000 building tree 844 of 1000 building tree 845 of 1000 building tree 846 of 1000 building tree 847 of 1000 building tree 848 of 1000 building tree 849 of 1000 building tree 850 of 1000 building tree 851 of 1000 building tree 852 of 1000 building tree 853 of 1000 building tree 854 of 1000 building tree 855 of 1000 building tree 856 of 1000 building tree 857 of 1000 building tree 858 of 1000building tree 859 of 1000 building tree 860 of 1000 building tree 861 of 1000 building tree 862 of 1000 building tree 863 of 1000 building tree 864 of 1000 building tree 865 of 1000 building tree 866 of 1000 building tree 867 of 1000 building tree 868 of 1000 building tree 869 of 1000
[Parallel(n_jobs=-1)]: Done 858 tasks | elapsed: 28.7min
building tree 870 of 1000 building tree 871 of 1000 building tree 872 of 1000 building tree 873 of 1000 building tree 874 of 1000 building tree 875 of 1000 building tree 876 of 1000 building tree 877 of 1000 building tree 878 of 1000 building tree 879 of 1000 building tree 880 of 1000 building tree 881 of 1000 building tree 882 of 1000 building tree 883 of 1000 building tree 884 of 1000 building tree 885 of 1000 building tree 886 of 1000 building tree 887 of 1000 building tree 888 of 1000 building tree 889 of 1000 building tree 890 of 1000 building tree 891 of 1000 building tree 892 of 1000 building tree 893 of 1000 building tree 894 of 1000building tree 895 of 1000 building tree 896 of 1000 building tree 897 of 1000 building tree 898 of 1000 building tree 899 of 1000 building tree 900 of 1000 building tree 901 of 1000 building tree 902 of 1000 building tree 903 of 1000 building tree 904 of 1000 building tree 905 of 1000 building tree 906 of 1000 building tree 907 of 1000 building tree 908 of 1000 building tree 909 of 1000 building tree 910 of 1000 building tree 911 of 1000 building tree 912 of 1000
[Parallel(n_jobs=-1)]: Done 901 tasks | elapsed: 30.2min
building tree 913 of 1000 building tree 914 of 1000 building tree 915 of 1000 building tree 916 of 1000 building tree 917 of 1000 building tree 918 of 1000 building tree 919 of 1000 building tree 920 of 1000 building tree 921 of 1000 building tree 922 of 1000 building tree 923 of 1000 building tree 924 of 1000 building tree 925 of 1000 building tree 926 of 1000 building tree 927 of 1000 building tree 928 of 1000 building tree 929 of 1000 building tree 930 of 1000 building tree 931 of 1000 building tree 932 of 1000 building tree 933 of 1000 building tree 934 of 1000 building tree 935 of 1000 building tree 936 of 1000 building tree 937 of 1000 building tree 938 of 1000 building tree 939 of 1000 building tree 940 of 1000 building tree 941 of 1000 building tree 942 of 1000 building tree 943 of 1000 building tree 944 of 1000 building tree 945 of 1000 building tree 946 of 1000 building tree 947 of 1000 building tree 948 of 1000 building tree 949 of 1000 building tree 950 of 1000 building tree 951 of 1000 building tree 952 of 1000 building tree 953 of 1000 building tree 954 of 1000 building tree 955 of 1000
[Parallel(n_jobs=-1)]: Done 944 tasks | elapsed: 31.6min
building tree 956 of 1000 building tree 957 of 1000 building tree 958 of 1000 building tree 959 of 1000 building tree 960 of 1000 building tree 961 of 1000 building tree 962 of 1000 building tree 963 of 1000 building tree 964 of 1000 building tree 965 of 1000 building tree 966 of 1000 building tree 967 of 1000 building tree 968 of 1000 building tree 969 of 1000 building tree 970 of 1000 building tree 971 of 1000 building tree 972 of 1000 building tree 973 of 1000 building tree 974 of 1000 building tree 975 of 1000 building tree 976 of 1000 building tree 977 of 1000 building tree 978 of 1000 building tree 979 of 1000 building tree 980 of 1000 building tree 981 of 1000 building tree 982 of 1000 building tree 983 of 1000 building tree 984 of 1000 building tree 985 of 1000 building tree 986 of 1000 building tree 987 of 1000 building tree 988 of 1000 building tree 989 of 1000 building tree 990 of 1000 building tree 991 of 1000 building tree 992 of 1000 building tree 993 of 1000 building tree 994 of 1000 building tree 995 of 1000 building tree 996 of 1000 building tree 997 of 1000 building tree 998 of 1000 building tree 999 of 1000 building tree 1000 of 1000
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 33.3min finished
RandomForestClassifier(bootstrap=False, class_weight=None, criterion='entropy',
max_depth=50, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=2, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=-1, oob_score=False, random_state=None,
verbose=10, warm_start=False)
_jl.dump(best_model, "../models/best_Random_Forest_real.joblib")
['../models/best_Random_Forest_real.joblib']
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set = test_set.drop(columns=[*_cols_bin, *_cols_std])
test_set.shape
(153993, 754)
# test classifier
predic = best_model.predict(test_set.drop(columns=['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
print("predictions:\n", predic)
[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 1 tasks | elapsed: 0.2s [Parallel(n_jobs=12)]: Done 8 tasks | elapsed: 0.2s [Parallel(n_jobs=12)]: Done 17 tasks | elapsed: 0.6s [Parallel(n_jobs=12)]: Done 26 tasks | elapsed: 0.9s [Parallel(n_jobs=12)]: Done 37 tasks | elapsed: 1.1s [Parallel(n_jobs=12)]: Done 48 tasks | elapsed: 1.4s [Parallel(n_jobs=12)]: Done 61 tasks | elapsed: 1.8s [Parallel(n_jobs=12)]: Done 74 tasks | elapsed: 2.1s [Parallel(n_jobs=12)]: Done 89 tasks | elapsed: 2.4s [Parallel(n_jobs=12)]: Done 104 tasks | elapsed: 2.7s [Parallel(n_jobs=12)]: Done 121 tasks | elapsed: 3.2s [Parallel(n_jobs=12)]: Done 138 tasks | elapsed: 3.6s [Parallel(n_jobs=12)]: Done 157 tasks | elapsed: 4.1s [Parallel(n_jobs=12)]: Done 176 tasks | elapsed: 4.5s [Parallel(n_jobs=12)]: Done 197 tasks | elapsed: 5.1s [Parallel(n_jobs=12)]: Done 218 tasks | elapsed: 5.6s [Parallel(n_jobs=12)]: Done 241 tasks | elapsed: 6.1s [Parallel(n_jobs=12)]: Done 264 tasks | elapsed: 6.7s [Parallel(n_jobs=12)]: Done 289 tasks | elapsed: 7.3s [Parallel(n_jobs=12)]: Done 314 tasks | elapsed: 7.9s [Parallel(n_jobs=12)]: Done 341 tasks | elapsed: 8.7s [Parallel(n_jobs=12)]: Done 368 tasks | elapsed: 9.3s [Parallel(n_jobs=12)]: Done 397 tasks | elapsed: 10.0s [Parallel(n_jobs=12)]: Done 426 tasks | elapsed: 10.7s [Parallel(n_jobs=12)]: Done 457 tasks | elapsed: 11.5s [Parallel(n_jobs=12)]: Done 488 tasks | elapsed: 12.2s [Parallel(n_jobs=12)]: Done 521 tasks | elapsed: 13.0s [Parallel(n_jobs=12)]: Done 554 tasks | elapsed: 13.8s [Parallel(n_jobs=12)]: Done 589 tasks | elapsed: 14.6s [Parallel(n_jobs=12)]: Done 624 tasks | elapsed: 15.5s [Parallel(n_jobs=12)]: Done 661 tasks | elapsed: 16.4s [Parallel(n_jobs=12)]: Done 698 tasks | elapsed: 17.2s [Parallel(n_jobs=12)]: Done 737 tasks | elapsed: 18.1s [Parallel(n_jobs=12)]: Done 776 tasks | elapsed: 19.1s [Parallel(n_jobs=12)]: Done 817 tasks | elapsed: 20.0s [Parallel(n_jobs=12)]: Done 858 tasks | elapsed: 21.0s [Parallel(n_jobs=12)]: Done 901 tasks | elapsed: 22.1s [Parallel(n_jobs=12)]: Done 944 tasks | elapsed: 23.0s [Parallel(n_jobs=12)]: Done 1000 out of 1000 | elapsed: 24.3s finished
predictions: [0 0 1 ... 0 1 0]
set(predic)
{0, 1}
# evaluate classifier
print("Report for Random Forest classifier:")
print(_classification_report(test_set['likes'], predic))
print("Accuracy for Random Forest Classifier:", _accuracy_score(test_set['likes'], predic)*100)
Report for Random Forest classifier:
precision recall f1-score support
0 0.70 0.39 0.50 50930
1 0.75 0.92 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.65 0.67 153993
weighted avg 0.74 0.74 0.72 153993
Accuracy for Random Forest Classifier: 74.34039209574462
# Confusion matrix for Random Forest
print("Confusion Matrix for Random Forest: ")
_confusion_matrix(test_set['likes'], predic)
Confusion Matrix for Random Forest:
array([[20055, 30875],
[ 8639, 94424]], dtype=int64)
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], predic)
_plt.plot(fpr,tpr)
_plt.xlim([0.0,1.0])
_plt.ylim([0.0,1.0])
_plt.title("Random Forest ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()
train_set = _pd.read_pickle('../dataset/m2_n9/model_train_set_3.pickle')
train_set.shape
(558386, 787)
train_set = train_set.drop(columns=[*_cols_bin, *_cols_std])
train_set.shape
(558386, 754)
# Number of neurons for hiddens layer
number_samples, number_features = train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']).shape
number_input_neurons = number_features
number_output_neurons = 1
number_train_semples = number_samples
alpha = 7
number_hidden_neurons = round(number_train_semples / (alpha * (number_input_neurons + number_output_neurons)))
print(number_hidden_neurons)
106
classifier = _Sequential()
# First Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal', input_dim = number_features))
# Second Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Third Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fourth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Fifth Hidden Layer
classifier.add(_Dense(number_hidden_neurons, activation = 'relu', kernel_initializer = 'random_normal'))
# Output Layer
classifier.add(_Dense(1, activation = 'sigmoid', kernel_initializer = 'random_normal'))
# Compiling the neural network
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the data to the training dataset
classifier.fit(train_set.drop(
columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']),
train_set['likes'], validation_split = 0.3, batch_size = 100, epochs = 100)
Train on 390870 samples, validate on 167516 samples Epoch 1/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5946 - acc: 0.6956 - val_loss: 0.5614 - val_acc: 0.7157 Epoch 2/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5553 - acc: 0.7237 - val_loss: 0.5431 - val_acc: 0.7351 Epoch 3/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5458 - acc: 0.7312 - val_loss: 0.5414 - val_acc: 0.7347 Epoch 4/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5414 - acc: 0.7351 - val_loss: 0.5421 - val_acc: 0.7341 Epoch 5/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5393 - acc: 0.7358 - val_loss: 0.5328 - val_acc: 0.7411 Epoch 6/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5361 - acc: 0.7378 - val_loss: 0.5395 - val_acc: 0.7355 Epoch 7/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5336 - acc: 0.7393 - val_loss: 0.5511 - val_acc: 0.7259 Epoch 8/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5327 - acc: 0.7404 - val_loss: 0.5319 - val_acc: 0.7417 Epoch 9/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5313 - acc: 0.7411 - val_loss: 0.5283 - val_acc: 0.7466 Epoch 10/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5300 - acc: 0.7423 - val_loss: 0.5301 - val_acc: 0.7469 Epoch 11/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5289 - acc: 0.7431 - val_loss: 0.5323 - val_acc: 0.7391 Epoch 12/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5282 - acc: 0.7433 - val_loss: 0.5353 - val_acc: 0.7420 Epoch 13/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5273 - acc: 0.7442 - val_loss: 0.5278 - val_acc: 0.7451 Epoch 14/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5261 - acc: 0.7449 - val_loss: 0.5282 - val_acc: 0.7424 Epoch 15/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5255 - acc: 0.7452 - val_loss: 0.5283 - val_acc: 0.7447 Epoch 16/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5248 - acc: 0.7460 - val_loss: 0.5224 - val_acc: 0.7485 Epoch 17/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5240 - acc: 0.7463 - val_loss: 0.5294 - val_acc: 0.7475 Epoch 18/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5233 - acc: 0.7464 - val_loss: 0.5351 - val_acc: 0.7403 Epoch 19/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5227 - acc: 0.7473 - val_loss: 0.5269 - val_acc: 0.7449 Epoch 20/100 390870/390870 [==============================] - 30s 76us/step - loss: 0.5221 - acc: 0.7478 - val_loss: 0.5241 - val_acc: 0.7486 Epoch 21/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5221 - acc: 0.7480 - val_loss: 0.5232 - val_acc: 0.7488 Epoch 22/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5209 - acc: 0.7480 - val_loss: 0.5202 - val_acc: 0.7505 Epoch 23/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5214 - acc: 0.7482 - val_loss: 0.5321 - val_acc: 0.7448 Epoch 24/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5197 - acc: 0.7491 - val_loss: 0.5218 - val_acc: 0.7495 Epoch 25/100 390870/390870 [==============================] - 30s 77us/step - loss: 0.5194 - acc: 0.7486 - val_loss: 0.5312 - val_acc: 0.7426 Epoch 26/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5186 - acc: 0.7492 - val_loss: 0.5195 - val_acc: 0.7495 Epoch 27/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5187 - acc: 0.7494 - val_loss: 0.5298 - val_acc: 0.7440 Epoch 28/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5180 - acc: 0.7498 - val_loss: 0.5346 - val_acc: 0.7420 Epoch 29/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5180 - acc: 0.7494 - val_loss: 0.5221 - val_acc: 0.7502 Epoch 30/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5176 - acc: 0.7495 - val_loss: 0.5265 - val_acc: 0.7498 Epoch 31/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5173 - acc: 0.7504 - val_loss: 0.5224 - val_acc: 0.7479 Epoch 32/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5174 - acc: 0.7503 - val_loss: 0.5295 - val_acc: 0.7468 Epoch 33/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5172 - acc: 0.7500 - val_loss: 0.5182 - val_acc: 0.7502 Epoch 34/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5171 - acc: 0.7502 - val_loss: 0.5211 - val_acc: 0.7487 Epoch 35/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5165 - acc: 0.7501 - val_loss: 0.5218 - val_acc: 0.7477 Epoch 36/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5168 - acc: 0.7505 - val_loss: 0.5328 - val_acc: 0.7411 Epoch 37/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5158 - acc: 0.7509 - val_loss: 0.5214 - val_acc: 0.7514 Epoch 38/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5151 - acc: 0.7518 - val_loss: 0.5209 - val_acc: 0.7505 Epoch 39/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5148 - acc: 0.7513 - val_loss: 0.5233 - val_acc: 0.7490 Epoch 40/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5147 - acc: 0.7516 - val_loss: 0.5204 - val_acc: 0.7511 Epoch 41/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5145 - acc: 0.7518 - val_loss: 0.5220 - val_acc: 0.7513 Epoch 42/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5154 - acc: 0.7507 - val_loss: 0.5218 - val_acc: 0.7492 Epoch 43/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5195 - acc: 0.7483 - val_loss: 0.5212 - val_acc: 0.7476 Epoch 44/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5140 - acc: 0.7519 - val_loss: 0.5210 - val_acc: 0.7519 Epoch 45/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5137 - acc: 0.7521 - val_loss: 0.5206 - val_acc: 0.7504 Epoch 46/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5133 - acc: 0.7524 - val_loss: 0.5250 - val_acc: 0.7488 Epoch 47/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5138 - acc: 0.7519 - val_loss: 0.5187 - val_acc: 0.7510 Epoch 48/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5134 - acc: 0.7523 - val_loss: 0.5319 - val_acc: 0.7428 Epoch 49/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5132 - acc: 0.7525 - val_loss: 0.5208 - val_acc: 0.7507 Epoch 50/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5155 - acc: 0.7523 - val_loss: 0.5569 - val_acc: 0.7311 Epoch 51/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5120 - acc: 0.7532 - val_loss: 0.5189 - val_acc: 0.7510 Epoch 52/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5120 - acc: 0.7526 - val_loss: 0.5177 - val_acc: 0.7522 Epoch 53/100 390870/390870 [==============================] - 29s 75us/step - loss: 0.5143 - acc: 0.7511 - val_loss: 0.5204 - val_acc: 0.7510 Epoch 54/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5175 - acc: 0.7495 - val_loss: 0.5230 - val_acc: 0.7493 Epoch 55/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5123 - acc: 0.7526 - val_loss: 0.5230 - val_acc: 0.7488 Epoch 56/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5128 - acc: 0.7524 - val_loss: 0.5350 - val_acc: 0.7442 Epoch 57/100 390870/390870 [==============================] - 28s 72us/step - loss: 0.5118 - acc: 0.7529 - val_loss: 0.5317 - val_acc: 0.7469 Epoch 58/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5110 - acc: 0.7542 - val_loss: 0.5214 - val_acc: 0.7471 Epoch 59/100 390870/390870 [==============================] - 28s 71us/step - loss: 0.5110 - acc: 0.7539 - val_loss: 0.5240 - val_acc: 0.7492 Epoch 60/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5103 - acc: 0.7543 - val_loss: 0.5316 - val_acc: 0.7407 Epoch 61/100 390870/390870 [==============================] - 27s 69us/step - loss: 0.5104 - acc: 0.7538 - val_loss: 0.5214 - val_acc: 0.7499 Epoch 62/100 390870/390870 [==============================] - 27s 68us/step - loss: 0.5103 - acc: 0.7541 - val_loss: 0.5351 - val_acc: 0.7428 Epoch 63/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5100 - acc: 0.7542 - val_loss: 0.5160 - val_acc: 0.7526 Epoch 64/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5102 - acc: 0.7543 - val_loss: 0.5191 - val_acc: 0.7527 Epoch 65/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5098 - acc: 0.7543 - val_loss: 0.5195 - val_acc: 0.7500 Epoch 66/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5098 - acc: 0.7541 - val_loss: 0.5231 - val_acc: 0.7504 Epoch 67/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5095 - acc: 0.7546 - val_loss: 0.5178 - val_acc: 0.7528 Epoch 68/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5092 - acc: 0.7546 - val_loss: 0.5310 - val_acc: 0.7401 Epoch 69/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5087 - acc: 0.7551 - val_loss: 0.5192 - val_acc: 0.7500 Epoch 70/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5090 - acc: 0.7546 - val_loss: 0.5217 - val_acc: 0.7496 Epoch 71/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5088 - acc: 0.7550 - val_loss: 0.5210 - val_acc: 0.7502 Epoch 72/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5087 - acc: 0.7549 - val_loss: 0.5282 - val_acc: 0.7473 Epoch 73/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5090 - acc: 0.7548 - val_loss: 0.5350 - val_acc: 0.7392 Epoch 74/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5076 - acc: 0.7550 - val_loss: 0.5198 - val_acc: 0.7528 Epoch 75/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5092 - acc: 0.7553 - val_loss: 0.5231 - val_acc: 0.7496 Epoch 76/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5074 - acc: 0.7556 - val_loss: 0.5198 - val_acc: 0.7512 Epoch 77/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5079 - acc: 0.7553 - val_loss: 0.5212 - val_acc: 0.7504 Epoch 78/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5110 - acc: 0.7550 - val_loss: 0.5387 - val_acc: 0.7505 Epoch 79/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5082 - acc: 0.7559 - val_loss: 0.5262 - val_acc: 0.7460 Epoch 80/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5081 - acc: 0.7551 - val_loss: 0.5198 - val_acc: 0.7502 Epoch 81/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5176 - acc: 0.7486 - val_loss: 0.5226 - val_acc: 0.7471 Epoch 82/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5117 - acc: 0.7525 - val_loss: 0.5283 - val_acc: 0.7431 Epoch 83/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5130 - acc: 0.7523 - val_loss: 0.5443 - val_acc: 0.7500 Epoch 84/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5188 - acc: 0.7455 - val_loss: 0.5171 - val_acc: 0.7527 Epoch 85/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5097 - acc: 0.7541 - val_loss: 0.5418 - val_acc: 0.7437 Epoch 86/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5095 - acc: 0.7544 - val_loss: 0.5177 - val_acc: 0.7529 Epoch 87/100 390870/390870 [==============================] - 27s 70us/step - loss: 0.5082 - acc: 0.7550 - val_loss: 0.5190 - val_acc: 0.7515 Epoch 88/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5073 - acc: 0.7560 - val_loss: 0.5881 - val_acc: 0.7489 Epoch 89/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5073 - acc: 0.7563 - val_loss: 0.5170 - val_acc: 0.7505 Epoch 90/100 390870/390870 [==============================] - 26s 68us/step - loss: 0.5073 - acc: 0.7556 - val_loss: 0.5199 - val_acc: 0.7511 Epoch 91/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5070 - acc: 0.7558 - val_loss: 0.5236 - val_acc: 0.7519 Epoch 92/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5073 - acc: 0.7557 - val_loss: 0.5192 - val_acc: 0.7510 Epoch 93/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5066 - acc: 0.7564 - val_loss: 0.5225 - val_acc: 0.7478 Epoch 94/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5062 - acc: 0.7567 - val_loss: 0.5167 - val_acc: 0.7525 Epoch 95/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5058 - acc: 0.7565 - val_loss: 0.5262 - val_acc: 0.7433 Epoch 96/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5059 - acc: 0.7566 - val_loss: 0.5157 - val_acc: 0.7535 Epoch 97/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5055 - acc: 0.7569 - val_loss: 0.5194 - val_acc: 0.7516 Epoch 98/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5059 - acc: 0.7561 - val_loss: 0.5254 - val_acc: 0.7454 Epoch 99/100 390870/390870 [==============================] - 26s 66us/step - loss: 0.5057 - acc: 0.7567 - val_loss: 0.5354 - val_acc: 0.7492 Epoch 100/100 390870/390870 [==============================] - 26s 67us/step - loss: 0.5054 - acc: 0.7567 - val_loss: 0.5189 - val_acc: 0.7509
<keras.callbacks.History at 0x1b77226d3c8>
# Save trained model
classifier.save("../models/trained_deep_neural_network_real.h5")
# evaluation of the model training
evaluation = classifier.evaluate(train_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']), train_set['likes'])
print(evaluation)
558386/558386 [==============================] - 17s 30us/step [0.5069156529383092, 0.7561507630928384]
del train_set
test_set = _pd.read_pickle('../dataset/m2_n9/model_test_set_3.pickle')
test_set = test_set.drop(columns=[*_cols_bin, *_cols_std])
test_set.shape
(153993, 754)
prediction = classifier.predict(test_set.drop(columns = ['likes', 'stars_review', 'review_id', 'user_id', 'business_id']))
binary_prediction = _binarize(prediction, threshold = 0.5)
binary_prediction
array([[1.],
[0.],
[1.],
...,
[0.],
[1.],
[0.]], dtype=float32)
set(binary_prediction[:,0])
{0.0, 1.0}
print("Report for Deep Learning approach:")
print(_classification_report(test_set['likes'], binary_prediction))
print("Accuracy for Deep Learning approach:", _accuracy_score(test_set['likes'], binary_prediction) * 100)
Report for Deep Learning approach:
precision recall f1-score support
0 0.70 0.39 0.51 50930
1 0.75 0.92 0.83 103063
accuracy 0.74 153993
macro avg 0.73 0.66 0.67 153993
weighted avg 0.74 0.74 0.72 153993
Accuracy for Deep Learning approach: 74.43974726123915
matrix = _confusion_matrix(test_set['likes'], binary_prediction)
print(matrix)
[[20079 30851] [ 8510 94553]]
# draw ROC curve
fpr, tpr, thresholds = _roc_curve(test_set['likes'], binary_prediction)
_plt.plot(fpr, tpr)
_plt.xlim([0.0, 1.0])
_plt.ylim([0.0, 1.0])
_plt.title("Neural Network ROC curve")
_plt.xlabel("False Positive")
_plt.ylabel("True Positive")
_plt.grid(True)
_plt.show()
_del_all()